Quantitative and qualitative MRI evaluation of cerebral small vessel disease in an elderly population: a longitudinal study.

Background Cerebral white matter hyperintensities (WMHs), lacunes, and microbleeds are seen on magnetic resonance imaging (MRI) in small vessel disease (SVD). Purpose To assess SVD on MRI and its evolution over five years in an elderly population and to investigate whether relative cerebral blood flow (rCBF) at baseline was related to the progression of white matter (WM) lesions. Material and Methods In a population-based study, 406 participants aged 75 years underwent morphological MRI of the brain and 252 of them again at age 80 years. At age 75 years, a perfusion scan was also done. WMHs were evaluated qualitatively (visual scoring) and quantitatively (CASCADE software). Lacunes and microbleeds were counted. Results A significant progression of the WMH score and WMH volume occurred over five years ( P < 0.0001). New lacunes were seen in 10%. Participants with new lacunes at age 80 years showed a more pronounced increase in WMHs (P < 0.0001). Microbleeds were present in 14% at age 75 years. The visual WMH score was significantly associated with the presence of microbleeds ( P < 0.0001). There was no relationship between total WM rCBF and WMH volume at age 75 years, and no significant associations between regional or total rCBF at age 75 years and changes in WMH volume over five years. The total WM and GM volume decreased significantly between the ages of 75 and 80 years ( P < 0.0001). Conclusion MRI manifestations of SVD progressed over five years in an elderly population (age range = 75-80 years). rCBF was not associated with WMH volume or progression of WMH volume.

Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study.

BACKGROUND AND PURPOSE: In vivoidentification of white matter lesions plays a key-role in evaluation of patients with multiple sclerosis (MS). Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations is lacking. In this work, five research-domain automated segmentation methods were evaluated using a multi-center MS dataset. METHODS: 70 MS patients (median EDSS of 2.0 [range 0.0-6.5]) were included from a six-center dataset of the MAGNIMS Study Group (www.magnims.eu) which included 2D FLAIR and 3D T1 images with manual lesion segmentation as a reference. Automated lesion segmentations were produced using five algorithms: Cascade; Lesion Segmentation Toolbox (LST) with both the Lesion growth algorithm (LGA) and the Lesion prediction algorithm (LPA); Lesion-Topology preserving Anatomical Segmentation (Lesion-TOADS); and k-Nearest Neighbor with Tissue Type Priors (kNN-TTP). Main software parameters were optimized using a training set (N = 18), and formal testing was performed on the remaining patients (N = 52). To evaluate volumetric agreement with the reference segmentations, intraclass correlation coefficient (ICC) as well as mean difference in lesion volumes between the automated and reference segmentations were calculated. The Similarity Index (SI), False Positive (FP) volumes and False Negative (FN) volumes were used to examine spatial agreement. All analyses were repeated using a leave-one-center-out design to exclude the center of interest from the training phase to evaluate the performance of the method on 'unseen' center. RESULTS: Compared to the reference mean lesion volume (4.85 ± 7.29 mL), the methods displayed a mean difference of 1.60 ± 4.83 (Cascade), 2.31 ± 7.66 (LGA), 0.44 ± 4.68 (LPA), 1.76 ± 4.17 (Lesion-TOADS) and -1.39 ± 4.10 mL (kNN-TTP). The ICCs were 0.755, 0.713, 0.851, 0.806 and 0.723, respectively. Spatial agreement with reference segmentations was higher for LPA (SI = 0.37 ± 0.23), Lesion-TOADS (SI = 0.35 ± 0.18) and kNN-TTP (SI = 0.44 ± 0.14) than for Cascade (SI = 0.26 ± 0.17) or LGA (SI = 0.31 ± 0.23). All methods showed highly similar results when used on data from a center not used in software parameter optimization. CONCLUSION: The performance of the methods in this multi-center MS dataset was moderate, but appeared to be robust even with new datasets from centers not included in training the automated methods.

Reproducible segmentation of white matter hyperintensities using a new statistical definition.

OBJECTIVES: We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. MATERIALS AND METHODS: T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer's disease) were used. The segmentation was performed using a proposed definition for WMH based on the one-tailed Kolmogorov-Smirnov test. RESULTS: The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85-0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83-0.94) that exceeded intra-rater similarity (Dice 0.75-0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed definition has better accuracy and reproducibility in the test dataset used. CONCLUSION: Overall, the presented definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility.

Corpus callosum atrophy is strongly associated with cognitive impairment in multiple sclerosis: Results of a 17-year longitudinal study.

BACKGROUND: Cognitive impairment is common in multiple sclerosis (MS) and may be subtle. The corpus callosum is essential for connectivity-demanding cognitive tasks and is significantly affected in MS, therefore it may serve as a marker for cognitive function. OBJECTIVE: The objective of this paper is to longitudinally study the normalized corpus callosum area (nCCA) as a marker of cognitive function and disability in MS. METHODS: Thirty-seven MS patients were followed from 1996 with follow-ups in 2004 and 2013. A healthy matched control group was recruited. The Expanded Disability Status Scale (EDSS) and Symbol Digit Modalities Test (SDMT) were assessed. The nCCA was measured on T2-weighted images. Volumetry was performed with FreeSurfer. RESULTS: Disease duration spanned five decades (1.6-46 years). Annual corpus callosal atrophy rate decreased with disease duration. nCCA was strongly correlated with SDMT (r = 0.793, p < 0.001) and moderately correlated with EDSS (r = -0.545, p < 0.001) after adjusting for disease duration, age and sex. The correlations of brain parenchymal fraction, white matter fraction, gray matter fraction and normalized lesion volume were less strong. CONCLUSIONS: The nCCA correlates well with physical and cognitive disability in time perspectives close to two decades, outperforming volumetric measurements. The nCCA is fast and could be feasible for clinical implementation where it may help identify patients in need of neuropsychological evaluation.

Cortical thickness and surface area relate to specific symptoms in early relapsing-remitting multiple sclerosis.

BACKGROUND: Cortical atrophy is common in early relapsing-remitting multiple sclerosis (RRMS). Whether this atrophy is caused by changes in cortical thickness or cortical surface area is not known, nor is their separate contributions to clinical symptoms. OBJECTIVES: To investigate the difference in cortical surface area, thickness and volume between early RRMS patients and healthy controls; and the relationship between these measures and neurological disability, cognitive decline, fatigue and depression. METHODS: RRMS patients (n = 61) underwent magnetic resonance imaging (MRI), neurological and neuropsychological examinations. We estimated cortical surface area, thickness and volume and compared them with matched healthy controls (n = 61). We estimated the correlations between clinical symptoms and cortical measures within the patient group. RESULTS: We found no differences in cortical surface area, but widespread differences in cortical thickness and volume between the groups. Neurological disability was related to regionally smaller cortical thickness and volume. Better verbal memory was related to regionally larger surface area; and better visuo-spatial memory, to regionally larger cortical volume. Higher depression scores and fatigue were associated with regionally smaller cortical surface area and volume. CONCLUSIONS: We found that cortical thickness, but not cortical surface area, is affected in early RRMS. We identified specific structural correlates to the main clinical symptoms in early RRMS.

Can measuring hippocampal atrophy with a fully automatic method be substantially less noisy than manual segmentation over both 1 and 3 years?

To quantify the "segmentation noise" of several widely used fully automatic methods for measuring longitudinal hippocampal atrophy in Alzheimer's disease and compare the results to the segmentation noise of manual segmentation over both 1 and 3 years. The segmentation noise of 5 longitudinal hippocampal atrophy measurement methods was quantified, including checking its Gaussianity, using 264 subjects from the ADNI1 back-to-back (BTB) data set over both 1 year and 3 year intervals. The segmentation methods were FreeSurfer 5.3.0 both cross sectional and longitudinal, FreeSurfer 6.0.0 longitudinal, MAPS-HBSI and FSL/FIRST 5.0.8. The BTB manual segmentation of 75 ADNI subjects from a previous study provided the manual distributions for comparison. All methods, including the manual segmentation, violated the Gaussianity assumption. Two methods, FreeSurfer 6.0.0 and MAPS-HBSI, had a segmentation noise substantially less than a surrogate for manual segmentation. FreeSurfer 5.3.0 longitudinal was confirmed as a surrogate for manual segmentation. The violation of the Gaussian assumption by the segmentation methods assessed, including manual, suggests results of previous studies that assumed Gaussian statistics without confirmation may need review. Fully automatic FreeSurfer 6.0.0 and MAPS-HBSI both have lower segmentation noise than manual requiring less than two thirds of the subjects to detect the same treatment effect.

Reproducibility of hippocampal atrophy rates measured with manual, FreeSurfer, AdaBoost, FSL/FIRST and the MAPS-HBSI methods in Alzheimer's disease.

The purpose of this study is to assess the reproducibility of hippocampal atrophy rate measurements of commonly used fully-automated algorithms in Alzheimer disease (AD). The reproducibility of hippocampal atrophy rate for FSL/FIRST, AdaBoost, FreeSurfer, MAPS independently and MAPS combined with the boundary shift integral (MAPS-HBSI) were calculated. Back-to-back (BTB) 3D T1-weighted MPRAGE MRI from the Alzheimer's Disease Neuroimaging Initiative (ADNI1) study at baseline and year one were used. Analysis on 3 groups of subjects was performed - 562 subjects at 1.5T, a 75 subject group that also had manual segmentation and 111 subjects at 3T. A simple and novel statistical test based on the binomial distribution was used that handled outlying data points robustly. Median hippocampal atrophy rates were -1.1%/year for healthy controls, -3.0%/year for mildly cognitively impaired and -5.1%/year for AD subjects. The best reproducibility was observed for MAPS-HBSI (1.3%), while the other methods tested had reproducibilities at least 50% higher at 1.5T and 3T which was statistically significant. For a clinical trial, MAPS-HBSI should require less than half the subjects of the other methods tested. All methods had good accuracy versus manual segmentation. The MAPS-HBSI method has substantially better reproducibility than the other methods considered.

Midlife CAIDE dementia risk score and dementia-related brain changes up to 30 years later on magnetic resonance imaging.

BACKGROUND: CAIDE Dementia Risk Score is a validated tool for estimating 20-year dementia risk in the general population based on a midlife risk profile. OBJECTIVE: To investigate the associations between CAIDE score and dementia-related brain changes up to 30 years later on magnetic resonance imaging (MRI). METHODS: Participants in the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) study were derived from random, population-based samples surveyed in 1972, 1977, 1982, or 1987. A first re-examination was conducted in 1998, and a second re-examination in 2005-2008 (total follow-up time up to 30 years). The MRI study population included 112 individuals with MRIs from the first re-examination, and a different group of 69 individuals with MRIs from the second re-examination. MRIs from 1998 were used to determine gray matter volume, and to visually rate white matter hyperintensities (WMH) of presumed vascular origin and medial temporal lobe atrophy (MTA). MRIs from 2005-2008 were used to assess cortical thickness, gray matter and WMH volume, and to visually rate MTA. CAIDE scores were calculated for participants in both re-examinations based on midlife sociodemographic and vascular factors and additionally apolipoprotein E status. RESULTS: Higher midlife CAIDE score was associated with more severe WMH 20 years later: RR (95% CI) was 1.69 (1.15-2.08); and with higher WMH volume (ß 0.27, p = 0.036) and higher MTA score (RR 1.91, 95% CI 1.16-2.34) up to 30 years later. CONCLUSION: CAIDE Dementia Risk Score in midlife was most consistently associated with WMH later in life. A relation with MTA was observed in individuals with longer follow-up time.

Reduced perfusion in white matter lesions in multiple sclerosis.

OBJECTIVE: To investigate dynamic susceptibility contrast (DSC) perfusion weighted imaging (PWI) in white matter lesions (WML) in patients with multiple sclerosis (MS), using automatically generated binary masks of brain tissue. BACKGROUND: WML in MS have in some studies demonstrated perfusion abnormalities compared to normal appearing white matter (NAWM), however perfusion changes in WML in MS have in general not been well documented. METHODS: DSC PWI was performed at 1.5 Tesla in 69 newly diagnosed MS patients. Parametric perfusion maps representing cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) were obtained. Binary masks of WML, white matter (WM) and grey matter (GM) were automatically generated and co-registered to the perfusion maps. The WML mask was manually edited and modified to correct for errors in the automatic lesion detection. Perfusion parameters were derived both from WML and NAWM using the manually modified WML mask, and using the original non-modified WML mask (with and without GM exclusion mask). Differences in perfusion measures between WML and NAWM were analyzed. RESULTS: CBF was significantly lower (p<0.001) and MTT significantly higher (p<0.001) in WML compared to NAWM. CBV did not show significant difference between WML and NAWM. The non-modified WML mask gave similar results as manually modified WML mask if the GM exclusion mask was used in the analysis. CONCLUSIONS: DSC PWI revealed lower CBF and higher MTT, consistent with reduced perfusion, in WML compared to NAWM in patients with early MS. Automatically generated binary masks are a promising tool in perfusion analysis of WML.

Eye and hand motor interactions with the Symbol Digit Modalities Test in early multiple sclerosis.

PURPOSE: Eye and hand motor dysfunction may be present early in the disease course of relapsing-remitting multiple sclerosis (RRMS), and can affect the results on visual and written cognitive tests. We aimed to test for differences in saccadic initiation time (SI time) between RRMS patients and healthy controls, and whether SI time and hand motor speed interacted with the written version of the Symbol Digit Modalities Test (wSDMT). METHODS: Patients with RRMS (N = 44, age 35.1 ± 7.3 years), time since diagnosis < 3 years and matched controls (N = 41, age 33.2 ± 6.8 years) were examined with ophthalmological, neurological and neuropsychological tests, as well as structural MRI (white matter lesion load (WMLL) and brainstem lesions), visual evoked potentials (VEP) and eye-tracker examinations of saccades. RESULTS: SI time was longer in RRMS than controls (p < 0.05). SI time was not related to the Paced Auditory Serial Addition Test (PASAT), WMLL or to the presence of brainstem lesions. 9 hole peg test (9HP) correlated significantly with WMLL (r = 0.58, p < 0.01). Both SI time and 9HP correlated negatively with the results of wSDMT (r = -0.32, p < 0.05, r = -0.47, p < 0.01), but none correlated with the results of PASAT. CONCLUSIONS: RRMS patients have an increased SI time compared to controls. Cognitive tests results, exemplified by the wSDMT, may be confounded by eye and hand motor function.

Comorbidity profile in dementia with Lewy bodies versus Alzheimer's disease: a linkage study between the Swedish Dementia Registry and the Swedish National Patient Registry.

INTRODUCTION: Compared to Alzheimer's disease (AD), dementia with Lewy bodies (DLB) is usually associated with a more complex clinical picture and higher burden of care. Yet, few investigations have been performed on comorbidities and risk factors of DLB. Therefore, we aimed to compare clinical risk factors and comorbidity profile in DLB and AD patients using two nationwide registries. METHODS: This is a linkage study between the Swedish dementia registry (SveDem) and the Swedish National Patient Registry conducted on 634 subjects with DLB and 9161 individuals with AD registered during the years 2007-2012. Comorbidity profile has been coded according to the International Classification of Diseases version 10 (ICD 10) in addition to the date of each event. The main chapters of the ICD-10, the Charlson score of comorbidities and a selected number of neuropsychiatric diseases were compared between the DLB and AD groups. Comorbidity was registered before and after the dementia diagnosis. RESULTS: "Mental and behavioral disorders", "diseases of the nervous system", "diseases of the eye and adnexa", diseases of the "circulatory", "respiratory", and "genitourinary" systems, "diseases of the skin and subcutaneous tissue" and "diseases of the musculoskeletal system and connective tissue" occurred more frequently in the DLB group after multivariate adjustment. Depression [adjusted OR = 2.12 (95%CI 1.49 to 3.03)] and migraine [adjusted OR = 3.65 (95%CI 1.48 to 9.0)] were more commonly recorded before the diagnosis of dementia in the DLB group. Following dementia diagnosis, ischemic stroke [adjusted OR = 1.89 (95%CI 1.21 to 2.96)] was more likely to happen among the DLB patients compared to the AD population. CONCLUSIONS: Our study indicated a worse comorbidity profile in DLB patients with higher occurrence of depression, stroke and migraine compared with the AD group. Deeper knowledge about the underlying mechanisms of these associations is needed to explore possible reasons for the different pattern of comorbidity profile in DLB compared to AD and their prognostic significance.

Coronary heart disease and cortical thickness, gray matter and white matter lesion volumes on MRI.

Coronary heart disease (CHD) has been linked with cognitive decline and dementia in several studies. CHD is strongly associated with blood pressure, but it is not clear how blood pressure levels or changes in blood pressure over time affect the relation between CHD and dementia-related pathology. The aim of this study was to investigate relations between CHD and cortical thickness, gray matter volume and white matter lesion (WML) volume on MRI, considering CHD duration and blood pressure levels from midlife to three decades later. The study population included 69 elderly at risk of dementia who participated in the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) study. CAIDE participants were examined in midlife, re-examined 21 years later, and then after additionally 7 years (in total up to 30 years follow-up). MRIs from the second re-examination were used to calculate cortical thickness, gray matter and WML volume. CHD diagnoses were obtained from the Finnish Hospital Discharge Register. Linear regression analyses were adjusted for age, sex, follow-up time and scanner type, and additionally total intracranial volume in GM volume analyses. Adding diabetes, cholesterol or smoking to the models did not influence the results. CHD was associated with lower thickness in multiple regions, and lower total gray matter volume, particularly in people with longer disease duration (>10 years). Associations between CHD, cortical thickness and gray matter volume were strongest in people with CHD and hypertension in midlife, and those with CHD and declining blood pressure after midlife. No association was found between CHD and WML volumes. Based on these results, long-term CHD seems to have detrimental effects on brain gray matter tissue, and these effects are influenced by blood pressure levels and their changes over time.

Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines.

White matter changes (WMC) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMC labor intensive and prone to subjective bias. We present a fast, fully automated method for WMC segmentation using a cascade of reduced support vector machines (SVMs) with active learning. Data of 102 subjects was used in this study. Two MRI sequences (T1-weighted and FLAIR) and masks of manually outlined WMC from each subject were used for the image analysis. The segmentation framework comprises pre-processing, classification (training and core segmentation) and post-processing. After pre-processing, the model was trained on two subjects and tested on the remaining 100 subjects. The effectiveness and robustness of the classification was assessed using the receiver operating curve technique. The cascade of SVMs segmentation framework outputted accurate results with high sensitivity (90%) and specificity (99.5%) values, with the manually outlined WMC as reference. An algorithm for the segmentation of WMC is proposed. This is a completely competitive and fast automatic segmentation framework, capable of using different input sequences, without changes or restrictions of the image analysis algorithm.