Validation of an MRI Rating Scale for Amyloid-Related Imaging Abnormalities.

INTRODUCTION: Immunotherapeutic agents against amyloid beta (Aß) are associated with adverse events, including amyloid-related imaging abnormalities with edema and effusion (ARIA-E). Recently, a magnetic resonance imaging (MRI) rating scale was developed for ARIA-E detection and classification. The aim of this study was to validate the use of this rating scale in a larger patient group with multiple raters. METHODS: MRI scans of 75 patients (29 with known ARIA-E and 46 control subjects) were analyzed by five neuroradiologists with different degrees of expertise, according to the ARIA-E rating scale. For each patient, we included a baseline and a follow-up fluid-attenuated inversion recovery image. Interrater agreement was calculated using intraclass correlation coefficient (ICC). RESULTS: On average, 4.1% of the ARIA-E cases were missed. We observed a high interrater agreement for scores of sulcal hyperintensity (SH; ICC = .915; 95% CI 85-95) and for the combined scores of the 2 ARIA-E findings, parenchymal hyperintensity (PH) and SH (ICC = .878; 95% CI 79-93). A slightly lower agreement for PH (ICC = .678; 95% CI 51-81) was noted. CONCLUSION: The ARIA-E rating scale is a simple tool to evaluate the extent of ARIA-E in patients recruited into Aß-lowering therapeutic trials. It shows high interrater agreement among raters with different degrees of expertise.

High-resolution T1-relaxation time mapping displays subtle, clinically relevant, gray matter damage in long-standing multiple sclerosis.

BACKGROUND: Gray matter (GM) pathology has high clinical relevance in multiple sclerosis (MS), but conventional magnetic resonance imaging (MRI) is insufficiently sensitive to visualize the rather subtle damage. OBJECTIVE: To investigate whether high spatial resolution T1-relaxation time (T1-RT) measurements can detect changes in the normal-appearing GM of patients with long-standing MS and whether these changes are associated with physical and cognitive impairment. METHODS: High spatial resolution (1.05 × 1.05 × 1.2 mm(3)) T1-RT measurements were performed at 3 T in 156 long-standing MS patients and 54 healthy controls. T1-RT histogram parameters in several regions were analyzed to investigate group differences. Stepwise linear regression analyses were used to assess the relation of T1-RT with physical and cognitive impairment. RESULTS: In both thalamus and cortex, T1-RT histogram skewness was higher in patients than controls. In the cortex, this was driven by the frontal and temporal lobes. No differences were found in other GM histogram parameters. Cortical skewness, thalamus volume, and average white matter (WM) lesion T1-RT emerged as the strongest predictors for cognitive performance (adjusted R(2) = 0.39). CONCLUSION: Subtle GM damage was present in the cortex and thalamus of MS patients, as indicated by increased T1-RT skewness. Increased cortical skewness emerged as an independent predictor of cognitive dysfunction.

Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant.

Grey matter atrophy is common in multiple sclerosis. However, in contrast with other neurodegenerative diseases, it is unclear whether grey matter atrophy in multiple sclerosis is a diffuse 'global' process or develops, instead, according to distinct anatomical patterns. Using source-based morphometry we searched for anatomical patterns of co-varying cortical thickness and assessed their relationships with white matter pathology, physical disability and cognitive functioning. Magnetic resonance imaging was performed at 3 T in 208 patients with long-standing multiple sclerosis (141 females; age = 53.7 ± 9.6 years; disease duration = 20.2 ± 7.1 years) and 60 age- and sex-matched healthy controls. Spatial independent component analysis was performed on cortical thickness maps derived from 3D T1-weighted images across all subjects to identify co-varying patterns. The loadings, which reflect the presence of each cortical thickness pattern in a subject, were compared between patients with multiple sclerosis and healthy controls with generalized linear models. Stepwise linear regression analyses were used to assess whether white matter pathology was associated with these loadings and to identify the cortical thickness patterns that predict measures of physical and cognitive dysfunction. Ten cortical thickness patterns were identified, of which six had significantly lower loadings in patients with multiple sclerosis than in controls: the largest loading differences corresponded to the pattern predominantly involving the bilateral temporal pole and entorhinal cortex, and the pattern involving the bilateral posterior cingulate cortex. In patients with multiple sclerosis, overall white matter lesion load was negatively associated with the loadings of these two patterns. The final model for physical dysfunction as measured with Expanded Disability Status Scale score (adjusted R(2) = 0.297; P < 0.001) included the predictors age, overall white matter lesion load, the loadings of two cortical thickness patterns (bilateral sensorimotor cortex and bilateral insula), and global cortical thickness. The final model predicting average cognition (adjusted R(2) = 0.469; P < 0.001) consisted of age, the loadings of two cortical thickness patterns (bilateral posterior cingulate cortex and bilateral temporal pole), overall white matter lesion load and normal-appearing white matter integrity. Although white matter pathology measures were part of the final clinical regression models, they explained limited incremental variance (to a maximum of 4%). Several cortical atrophy patterns relevant for multiple sclerosis were found. This suggests that cortical atrophy in multiple sclerosis occurs largely in a non-random manner and develops (at least partly) according to distinct anatomical patterns. In addition, these cortical atrophy patterns showed stronger associations with clinical (especially cognitive) dysfunction than global cortical atrophy.

Multi-parametric structural magnetic resonance imaging in relation to cognitive dysfunction in long-standing multiple sclerosis.

BACKGROUND: Cognitive deficits are common in multiple sclerosis. Most previous studies investigating the imaging substrate of cognitive deficits in multiple sclerosis included patients with relatively short disease durations and were limited to one modality/brain region. OBJECTIVE: To identify the strongest neuroimaging predictors for cognitive dysfunction in a large cohort of patients with long-standing multiple sclerosis. METHODS: Extensive neuropsychological testing and multimodal 3.0T MRI was performed in 202 patients with multiple sclerosis and 52 controls. Cognitive scores were compared between groups using Z-scores. Whole-brain, white matter, grey matter, deep grey matter and lesion volumes; cortical thickness, (juxta)cortical and cerebellar lesions; and extent and severity of diffuse white matter damage were measured. Stepwise linear regression was used to identify the strongest predictors for cognitive dysfunction. RESULTS: All cognitive domains were affected in patients. Patients showed extensive atrophy, focal pathology and damage in up to 75% of the investigated white matter. Associations between imaging markers and average cognition were two times stronger in cognitively impaired patients than in cognitively preserved patients. The final model for average cognition consisted of deep grey matter DGMV volume and fractional anisotropy severity (adjusted R²=0.490; p<0.001). CONCLUSION: From all imaging markers, deep grey matter atrophy and diffuse white matter damage emerged as the strongest predictors for cognitive dysfunction in long-standing multiple sclerosis.

Unraveling the neuroimaging predictors for motor dysfunction in long-standing multiple sclerosis.

OBJECTIVE: To find the strongest neuroimaging predictors for motor dysfunction using conventional and quantitative imaging measures focusing on the corticospinal tract (CST) in a large cohort of patients with long-standing multiple sclerosis (MS). METHODS: In this cross-sectional study, a wide spectrum of neuroimaging measures at the whole-brain, cervical, and CST level were analyzed in 195 patients with MS and 54 healthy controls. Motor function was assessed using the Expanded Disability Status Scale (EDSS), 9-Hole Peg Test, Timed 25-Foot Walk Test, and Multiple Sclerosis Walking Scale. Associations between damage in different parts of the motor system and motor functioning were assessed using stepwise linear regression. RESULTS: Patients had an average disease duration of 19.98 (±6.99) years and a median EDSS score of 4 (range: 1.0-8.0). EDSS score was associated with number of infratentorial and cervical cord lesions, lesion volume in the CST, and mean upper cervical cord area (adjusted R(2) = 0.403). Timed 25-Foot Walk Test score was associated with number of infratentorial lesions and cerebellar volume (adjusted R(2) = 0.150), 9-Hole Peg Test score with number of infratentorial lesions and thickness of the cortex connected to the CST (adjusted R(2) = 0.245), and Multiple Sclerosis Walking Scale with number of infratentorial and cervical lesions, thickness of the cortex connected to the CST, and mean upper cervical cord area (adjusted R(2) = 0.354). CONCLUSIONS: Motor dysfunction in MS has a complex substrate that cannot be ascribed to a single neuroimaging finding, but is the consequence of infratentorial and spinal cord damage, as well as damage in the CST.

Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images.

The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter settings.

Differential patterns of spinal cord and brain atrophy in NMO and MS.

OBJECTIVE: To investigate spinal cord and brain atrophy in neuromyelitis optica (NMO), and its relationship with other MRI measurements and clinical disability, compared with patients with multiple sclerosis (MS) and healthy controls (HC). METHODS: We recruited 35 patients with NMO, 35 patients with MS, and 35 HC, who underwent both spinal cord and brain MRI. Mean upper cervical cord area (MUCCA), brain parenchymal fraction (BPF), gray matter fraction (GMF), white matter fraction (WMF), and spinal cord and brain lesion loads were measured and compared among groups. Multivariate associations between MUCCA and brain volume measurement and clinical variables were assessed by partial correlations and multiple linear regression. RESULTS: Patients with NMO showed smaller MUCCA than HC (p = 0.004), and patients with MS had a trend of smaller MUCCA compared to HC (p = 0.07), with no significant difference between the patient groups. Patients with NMO showed lower BPF than HC, and patients with MS had lower BPF and GMF than patients with NMO. In NMO, MUCCA was correlated with Expanded Disability Status Scale score (EDSS), number of relapses, and total spinal cord lesion length, while in MS, MUCCA was correlated with WMF and EDSS. MUCCA was the only independent variable for predicting clinical disability measured by EDSS in NMO (R(2) = 0.55, p < 0.001) and MS (R(2) = 0.17, p = 0.013). CONCLUSION: NMO showed predominately spinal cord atrophy with mild brain atrophy, while MS demonstrated more brain atrophy, especially in the gray matter. MUCCA is the main MRI-derived parameter for explaining clinical disability in NMO and MS, and may serve as a potential biomarker for further clinical trials, especially in NMO.

Unraveling the relationship between regional gray matter atrophy and pathology in connected white matter tracts in long-standing multiple sclerosis.

INTRODUCTION: Gray matter (GM) atrophy is common in multiple sclerosis (MS), but the relationship with white matter (WM) pathology is largely unknown. Some studies found a co-occurrence in specific systems, but a regional analysis across the brain in different clinical phenotypes is necessary to further understand the disease mechanism underlying GM atrophy in MS. Therefore, we investigated the association between regional GM atrophy and pathology in anatomically connected WM tracts. METHODS: Conventional and diffusion tensor imaging was performed at 3T in 208 patients with long-standing MS and 60 healthy controls. Deep and cortical GM regions were segmented and quantified, and both lesion volumes and average normal appearing WM fractional anisotropy of their associated tracts were derived using an atlas obtained by probabilistic tractography in the controls. Linear regression was then performed to quantify the amount of regional GM atrophy that can be explained by WM pathology in the connected tract. RESULTS: MS patients showed extensive deep and cortical GM atrophy. Cortical atrophy was particularly present in frontal and temporal regions. Pathology in connected WM tracts statistically explained both regional deep and cortical GM atrophy in relapsing-remitting (RR) patients, but only deep GM atrophy in secondary-progressive (SP) patients. CONCLUSION: In RRMS patients, both deep and cortical GM atrophy were associated with pathology in connected WM tracts. In SPMS patients, only regional deep GM atrophy could be explained by pathology in connected WM tracts. This suggests that in SPMS patients cortical GM atrophy and WM damage are (at least partly) independent disease processes.

Disruption of structural and functional networks in long-standing multiple sclerosis.

Both gray matter atrophy and disruption of functional networks are important predictors for physical disability and cognitive impairment in multiple sclerosis (MS), yet their relationship is poorly understood. Graph theory provides a modality invariant framework to analyze patterns of gray matter morphology and functional coactivation. We investigated, how gray matter and functional networks were affected within the same MS sample and examined their interrelationship. Magnetic resonance imaging and magnetoencephalography (MEG) were performed in 102 MS patients and 42 healthy controls. Gray matter networks were computed at the group-level based on cortical thickness correlations between 78 regions across subjects. MEG functional networks were computed at the subject level based on the phase-lag index between time-series of regions in source-space. In MS patients, we found a more regular network organization for structural covariance networks and for functional networks in the theta band, whereas we found a more random network organization for functional networks in the alpha2 band. Correlation analysis revealed a positive association between covariation in thickness and functional connectivity in especially the theta band in MS patients, and these results could not be explained by simple regional gray matter thickness measurements. This study is a first multimodal graph analysis in a sample of MS patients, and our results suggest that a disruption of gray matter network topology is important to understand alterations in functional connectivity in MS as regional gray matter fails to take into account the inherent connectivity structure of the brain.

Mean upper cervical cord area (MUCCA) measurement in long-standing multiple sclerosis: relation to brain findings and clinical disability.

BACKGROUND: The majority of patients with multiple sclerosis (MS) present with spinal cord pathology. Spinal cord atrophy is thought to be a marker of disease severity, but in long-disease duration its relation to brain pathology and clinical disability is largely unknown. OBJECTIVE: Our aim was to investigate mean upper cervical cord area (MUCCA) in patients with long-standing MS and assess its relation to brain magnetic resonance imaging (MRI) measures and clinical disability. METHODS: MUCCA was measured in 196 MS patients and 55 healthy controls using 3DT1-weighted cervical images obtained at 3T MRI. Clinical disability was measured using the Expanded Disability Status Scale (EDSS), Nine-Hole-Peg test (9-HPT), and 25 feet Timed Walk Test (TWT). Stepwise linear regression was performed to assess the association between MUCCA and MRI measures, and between MUCCA and clinical disability. RESULTS: MUCCA was smaller (mean 11.7%) in MS patients compared with healthy controls (72.56±9.82 and 82.24±7.80 mm2 respectively; p<0.001), most prominently in male patients. MUCCA was associated with normalized brain volume, and number of cervical cord lesions. MUCCA was independently associated with EDSS, TWT, and 9-HPT. CONCLUSION: MUCCA was reduced in MS patients compared with healthy controls. It provides a relevant marker for clinical disability in long-standing disease, independent of other MRI measures.

What explains gray matter atrophy in long-standing multiple sclerosis?

PURPOSE: To identify the measures of focal and diffuse white matter (WM) abnormalities that are related to whole-brain, deep, and cortical gray matter (GM) atrophy in long-standing multiple sclerosis (MS). MATERIALS AND METHODS: The institutional review board approved the study; all subjects gave written informed consent. Magnetic resonance (MR) imaging was performed at 3 T in 208 patients with MS of long-standing duration (disease duration ≥ 10 years) and in 60 healthy control subjects. Normalized GM volume (NGMV), normalized WM volume (NWMV), normalized deep GM volume (NDGMV), cortical thickness, and normalized lesion volume (NLV) were quantified. Tissue integrity of normal-appearing WM (NAWM) and lesions was measured by using diffusion-tensor MR imaging. Multivariate associations between measures of GM atrophy and WM abnormalities were assessed in the patient group by using multiple linear regression. RESULTS: NGMV, NDGMV, and cortical thickness were reduced in patients with MS (all P < .001). The final model for NGMV consisted of NWMV, NLV, and patient age and sex (adjusted R(2) = 0.58, P < .001). NWMV, NLV, and patient sex were the explanatory variables for NDGMV (adjusted R(2) = 0.75, P < .001). The model for cortical thickness consisted of fractional anisotropy of NAWM, NLV, and patient age and sex (adjusted R(2) = 0.32, P < .001). The relationship between GM atrophy and WM abnormalities was weaker in primary and secondary progressive disease than in relapsing-remitting disease. CONCLUSION: Whole-brain and deep GM atrophy were particularly explained by WM atrophy and lesion volume, while cortical atrophy was associated with NAWM integrity loss. The weaker relationship between GM atrophy and WM abnormalities in patients with progressive disease might indicate a more independent neurodegenerative disease process in these patients.

Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs).

INTRODUCTION: The segmentation and volumetric quantification of white matter (WM) lesions play an important role in monitoring and studying neurological diseases such as multiple sclerosis (MS) or cerebrovascular disease. This is often interactively done using 2D magnetic resonance images. Recent developments in acquisition techniques allow for 3D imaging with much thinner sections, but the large number of images per subject makes manual lesion outlining infeasible. This warrants the need for a reliable automated approach. Here we aimed to improve k nearest neighbor (kNN) classification of WM lesions by optimizing intensity normalization and using spatial tissue type priors (TTPs). METHODS: The kNN-TTP method used kNN classification with 3.0 T 3DFLAIR and 3DT1 intensities as well as MNI-normalized spatial coordinates as features. Additionally, TTPs were computed by nonlinear registration of data from healthy controls. Intensity features were normalized using variance scaling, robust range normalization or histogram matching. The algorithm was then trained and evaluated using a leave-one-out experiment among 20 patients with MS against a reference segmentation that was created completely manually. The performance of each normalization method was evaluated both with and without TTPs in the feature set. Volumetric agreement was evaluated using intra-class coefficient (ICC), and voxelwise spatial agreement was evaluated using Dice similarity index (SI). Finally, the robustness of the method across different scanners and patient populations was evaluated using an independent sample of elderly subjects with hypertension. RESULTS: The intensity normalization method had a large influence on the segmentation performance, with average SI values ranging from 0.66 to 0.72 when no TTPs were used. Independent of the normalization method, the inclusion of TTPs as features increased performance particularly by reducing the lesion detection error. Best performance was achieved using variance scaled intensity features and including TTPs in the feature set: this yielded ICC = 0.93 and average SI = 0.75 ± 0.08. Validation of the method in an independent sample of elderly subjects with hypertension, yielded even higher ICC = 0.96 and SI = 0.84 ± 0.14. CONCLUSION: Adding TTPs increases the performance of kNN based MS lesion segmentation methods. Best performance was achieved using variance scaling for intensity normalization and including TTPs in the feature set, showing excellent agreement with the reference segmentations across a wide range of lesion severity, irrespective of the scanner used or the pathological substrate of the lesions.

Accelerated aging, decreased white matter integrity, and associated neuropsychological dysfunction 25 years after pediatric lymphoid malignancies.

PURPOSE: CNS-directed chemotherapy (CT) and cranial radiotherapy (CRT) for childhood acute lymphoblastic leukemia or lymphoma have various neurotoxic properties. This study aimed to assess their impact on the maturing brain 20 to 30 years after diagnosis, providing a much stronger perspective on long-term quality of life than previous studies. PATIENTS AND METHODS: Ninety-three patients treated between 1978 and 1990 at various intensities, with and without CRT, and 49 healthy controls were assessed with magnetic resonance diffusion tensor imaging (DTI) and neuropsychological tests. Differences in fractional anisotropy (FA)-a DTI measure describing white matter (WM) microstructure-were analyzed by using whole brain voxel-based analysis. RESULTS: CRT-treated survivors demonstrated significantly decreased FA compared with controls in frontal, parietal, and temporal WM tracts. Trends for lower FA were seen in the CT-treated survivors. Decreases in FA correlated well with neuropsychological dysfunction. In contrast to the CT group and controls, the CRT group showed a steep decline of FA with age at assessment. Younger age at cranial irradiation and higher dosage were associated with worse outcome of WM integrity. CONCLUSION: CRT-treated survivors show decreased WM integrity reflected by significantly decreased FA and associated neuropsychological dysfunction 25 years after treatment, although effects of CT alone seem mild. Accelerated aging of the brain and increased risk of early onset dementia are suspected after CRT, but not after CT.

Cortical imaging in multiple sclerosis: recent findings and 'grand challenges'.

PURPOSE OF REVIEW: This review summarizes recent advancements in cortical imaging in multiple sclerosis (MS). RECENT FINDINGS: New and advanced imaging techniques have been developed to improve cortical lesion detection, measure cortical atrophy and depict diffuse gray matter damage. New pulse sequences have been developed and (ultra) high-field strength has been proven to be advantageous in the visualization of cortical lesions. Unfortunately, there are still many cortical lesions that remain undetected on MR sequences, especially subpial lesions. Combining histopathological findings with MR imaging has provided us with more insight into MS pathogenesis. Cortical atrophy, deep gray matter atrophy and cortical lesions all have diagnostic and prognostic value. SUMMARY: New and advanced imaging techniques continue to play a pivotal role in the visualization and quantification of cortical damage in MS. It provides us with a better understanding of cognitive functioning and functional reorganization.

Long-term effects of cranial irradiation and intrathecal chemotherapy in treatment of childhood leukemia: a MEG study of power spectrum and correlated cognitive dysfunction.

BACKGROUND: Prophylaxis to prevent relapses in the central nervous system after childhood acute lymphoblastic leukemia (ALL) used to consist of both intrathecal chemotherapy (CT) and cranial irradiation (CRT). CRT was mostly abolished in the eighties because of its neurotoxicity, and replaced with more intensive intrathecal CT. In this study, a group of survivors treated with CRT before 1983 and another group treated without CRT thereafter are investigated 20-25 years later, giving a much stronger perspective on long-term quality of life than previous studies. The outcomes will help to better understand these groups' current needs and will aid in anticipating late effects of prophylactic CRT that is currently applied for other diseases. This study evaluates oscillatory neuronal activity in these long-term survivors. Power spectrum deviations are hypothesized to correlate with cognitive dysfunction. METHODS: Resting state eyes-closed magnetoencephalography (MEG) recordings were obtained from 14 ALL survivors treated with CT + CRT, 18 treated with CT alone and 35 controls. Relative spectral power was calculated in the δ, θ, α1, α2, ß and γ frequency bands. The Amsterdam Neuropsychological Tasks (ANT) program was used to assess cognition in the executive functions domain. MEG data and ANT scores were correlated. RESULTS: In the CT + CRT group, relative θ power was slightly increased (p = 0.069) and α2 power was significantly decreased (p = 0.006). The CT + CRT group performed worse on various cognitive tests. A deficiency in visuomotor accuracy, especially of the right hand, could be clearly associated with the deviating regional θ and α2 powers (0.471 < r < 0.697). A significant association between decreased regional α2 power and less attentional fluctuations was found for CT + CRT patients as well as controls (0.078 < r < 0.666). Patients treated with CT alone displayed a power spectrum similar to controls, except for a significantly increased level of left frontal α2 power (p = 0.030). CONCLUSIONS: The tendency towards global slowing of brain oscillatory activity, together with the fact that dementia has been reported as a late effect of CRT and the neuropsychological deficiencies currently present, suggest that the irradiated brain might be aging faster and could be at risk for early-onset dementia. The CT group showed no signs of early aging.