Charlotte Ryberg

Corpus callosum atrophy is associated with mental slowing and executive deficits in subjects with age-related white matter hyperintensities: the LADIS Study

Background: Previous research has indicated that corpus callosum atrophy is associated with global cognitive decline in neurodegenerative diseases, but few studies have investigated specific cognitive functions. Objective: To investigate the role of regional corpus callosum atrophy in mental speed, attention and executive functions in subjects with age-related white matter hyperintensities (WMH). Methods: In the Leukoaraiosis and Disability Study, 567 subjects with age-related WMH were examined with a detailed neuropsychological assessment and quantitative magnetic resonance imaging. The relationships of the total corpus callosum area and its subregions with cognitive performance were analysed using multiple linear regression, controlling for volume of WMH and other confounding factors. Results: Atrophy of the total corpus callosum area was associated with poor performance in tests assessing speed of mental processing—namely, trail making A and Stroop test parts I and II. Anterior, but not posterior, corpus callosum atrophy was associated with deficits of attention and executive functions as reflected by the symbol digit modalities and digit cancellation tests, as well as by the subtraction scores in the trail making and Stroop tests. Furthermore, semantic verbal fluency was related to the total corpus callosum area and the isthmus subregion. Conclusions: Corpus callosum atrophy seems to contribute to cognitive decline independently of age, education, coexisting WMH and stroke. Anterior corpus callosum atrophy is related to the frontal-lobe-mediated executive functions and attention, whereas overall corpus callosum atrophy is associated with the slowing of processing speed.

Clinical significance of corpus callosum atrophy in a mixed elderly population.

Corpus callosum (CC) is the main tract connecting the hemispheres, but the clinical significance of CC atrophy is poorly understood. The aim of this work was to investigate clinical and functional correlates of CC atrophy in subjects with age-related white matter changes (ARWMC). In 569 elderly subjects with ARWMC from the Leukoaraiosis And DISability (LADIS) study, the CC was segmented on the normalised mid-sagittal magnetic resonance imaging (MRI) slice and subdivided into five regions. Correlations between the CC areas and subjective memory complaints, mini mental state examination (MMSE) score, history of depression, geriatric depression scale (GDS) score, subjective gait difficulty, history of falls, walking speed, and total score on the short physical performance battery (SPPB) were analyzed. Significant correlations between CC atrophy and MMSE, SPPB, and walking speed were identified, and the CC areas were smaller in subjects with subjective gait difficulty. The correlations remained significant after correction for ARWMC grade. In conclusion, CC atrophy was independently associated with impaired global cognitive and motor function in subjects with ARWMC.

Reliability and sensitivity of visual scales versus volumetry for evaluating white matter hyperintensity progression.

Background: Investigating associations between the change of white matter hyperintensities (WMH) and clinical symptoms over time is crucial for establishing a causal relationship. However, the most suitable method for measuring WMH progression has not been established yet. We compared the reliability and sensitivity of cross-sectional and longitudinal visual scales with volumetry for measuring WMH progression. Methods: Twenty MRI scan pairs (interval 2 years) were included from the Amsterdam center of the LADIS study. Semi-automated volumetry of WMH was performed twice by one rater. Three cross-sectional scales (Fazekas Scale, Age-Related White Matter Changes Scale, Scheltens Scale) and two progression scales (Rotterdam Progression Scale, Schmidt Progression Scale) were scored by 4 and repeated by 2 raters. Results: Mean WMH volume (24.6 ± 27.9 ml at baseline) increased by 4.6 ± 5.1 ml [median volume change (range) = 2.7 (–0.6 to 15.7) ml]. Measuring volumetric change in WMH was reliable (intraobserver:intraclass coefficient = 0.88). All visual scales showed significant change of WMH over time, although the sensitivity was highest for both of the progression scales. Proportional volumetric change of WMH correlated best with the Rotterdam Progression Scale (Spearman’s r = 0.80, p < 0.001) and the Schmidt Progression Scale (Spearman’s r = 0.64, p < 0.01). Although all scales were reliable for assessment of WMH cross-sectionally, WMH progression assessment using visual scales was less reliable, except for the Rotterdam Progression scale which had moderate to good reliability [weighted Cohen’s ĸ = 0.63 (intraobserver), 0.59 (interobserver)]. Conclusion: To determine change in WMH, dedicated progression scales are more sensitive and/or reliable and correlate better with volumetric volume change than cross-sectional scales.

Corpus callosum atrophy as a predictor of age-related cognitive and motor impairment: a 3-year follow-up of the LADIS study cohort.

The aim of this 3-year follow-up study was to investigate whether corpus callosum (CC) atrophy may predict future motor and cognitive impairment in an elderly population. On baseline MRI from 563 subjects with age-related white matter changes (ARWMC) from the Leukoaraiosis And DISability (LADIS) study, the CC was segmented and subdivided into five anterior-posterior regions (CC1-CC5). Associations between the CC areas and decline in motor performance and cognitive functions over a 3-year period were analyzed. CC atrophy at baseline was significantly associated with impaired cognitive performance (p<0.01 for CC1, p<0.05 for CC5), motor function (p<0.05 for CC2 and CC5), and walking speed (p<0.01 for CC2 and CC5, p<0.05 for CC3 and total CC), and with development of dementia at 3 years (p<0.05 for CC1) after correction for appropriate confounders (ARWMC volume, atrophy, age, gender and handedness). In conclusion, CC atrophy, an indicator of reduced functional connectivity between cortical areas, seems to contribute, independently of ARWMC load, to future cognitive and motor decline in the elderly.

White Matter Changes Contribute to Corpus Callosum Atrophy in the Elderly: The LADIS Study

BACKGROUND AND PURPOSE: The corpus callosum (CC) is the most important structure involved in the transmission of interhemispheric information. The aim of this study was to investigate the potential correlation between regional age-related white matter changes (ARWMC) and atrophy of CC in elderly subjects. MATERIALS AND METHODS: In 578 subjects with ARWMC from the Leukoaraiosis And DISability (LADIS) study, the cross-sectional area of the CC was automatically segmented on the normalized midsagittal MR imaging section and subdivided into 5 regions. The ARWMC volumes were measured quantitatively by using a semiautomated technique and segmented into 6 brain regions. RESULTS: Significant correlation between the area of the rostrum and splenium regions of the CC and the ARWMC load in most brain regions was identified. This correlation persisted after correction for global atrophy. CONCLUSION: Increasing loads of ARWMC volume were significantly correlated with atrophy of the CC and its subregions in nondisabled elderly subjects with leukoaraiosis. However, the pattern of correlation between CC subregions and ARWMC was not specifically related to the topographic location of ARWMC. The results suggest that ARWMC may lead to a gradual loss of CC tissue.

Corpus Callosum Atrophy in Patients with Mild Alzheimer’s Disease

Background/Objectives: Several studies have found atrophy of the corpus callosum (CC) in patients with Alzheimer’s disease (AD). However, it remains unclear whether callosal atrophy is already present in the early stages of AD, and to what extent it may be associated with other structural changes in the brain, such as age-related white matter changes (ARWMC) and progression of the disease. Meth ods: Twenty-eight patients in the early stages of AD and 50 non-demented elderly subjects with varying degrees of ARWMC were investigated using MRI. The CC was assessed semi-automatically, and ARWMC were rated according to the Fazekas scale. Results: A significant difference in posterior CC size could be detected between non-demented elderly subjects and early stage AD patients. The sizes of the total CC, rostral body and splenium at baseline were correlated with change from baseline MMSE score after a 1-year follow-up in AD patients. There was no association between CC size and ARWMC. Conclusions: The present findings indicate that posterior CC atrophy is present in mild AD independently of ARWMC. Furthermore, CC atrophy may be associated with cognitive deterioration.

Sparse Decomposition and Modeling of Anatomical Shape Variation

Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counterparts if constructed carefully. In most medical applications, models are required to have both good statistical performance and a relevant clinical interpretation to be of value. Morphometry of the corpus callosum is one illustrative example. This paper presents a method for relating spatial features to clinical outcome data. A set of parsimonious variables is extracted using sparse principal component analysis, producing simple yet characteristic features. The relation of these variables with clinical data is then established using a regression model. The result may be visualized as patterns of anatomical variation related to clinical outcome. In the present application, landmark-based shape data of the corpus callosum is analyzed in relation to age, gender, and clinical tests of walking speed and verbal fluency. To put the data-driven sparse principal component method into perspective, we consider two alternative techniques, one where features are derived using a model-based wavelet approach, and one where the original variables are regressed directly on the outcome.

Mid-sagittal plane and mid-sagittal surface optimization in brain MRI using a local symmetry measure

This paper describes methods for automatic localization of the mid-sagittal plane (MSP) and mid-sagittal surface (MSS). The data used is a subset of the Leukoaraiosis And DISability (LADIS) study consisting of three-dimensional magnetic resonance brain data from 62 elderly subjects (age 66 to 84 years). Traditionally, the mid-sagittal plane is localized by global measures. However, this approach fails when the partitioning plane between the brain hemispheres does not coincide with the symmetry plane of the head. We instead propose to use a sparse set of profiles in the plane normal direction and maximize the local symmetry around these using a general-purpose optimizer. The plane is parameterized by azimuth and elevation angles along with the distance to the origin in the normal direction. This approach leads to solutions confirmed as the optimal MSP in 98 percent of the subjects. Despite the name, the mid-sagittal plane is not always planar, but a curved surface resulting in poor partitioning of the brain hemispheres. To account for this, this paper also investigates an optimization strategy which fits a thin-plate spline surface to the brain data using a robust least median of squares estimator. Albeit computationally more expensive, mid-sagittal surface fitting demonstrated convincingly better partitioning of curved brains into cerebral hemispheres.

Visual processing speed in old age.

Mental speed is a common concept in theories of cognitive aging, but it is difficult to get measures of the speed of a particular psychological process that are not confounded by the speed of other processes. We used Bundesens (1990) Theory of Visual Attention (TVA) to obtain specific estimates of processing speed in the visual system controlled for the influence of response latency and individual variations of the perception threshold. A total of 33 non-demented old people (69-87 years) were tested for the ability to recognize briefly presented letters. Performance was analyzed by the TVA model. Visual processing speed decreased approximately linearly with age and was on average halved from 70 to 85 years. Less dramatic aging effects were found for the perception threshold and the visual apprehension span. In the visual domain, cognitive aging seems to be most clearly related to reductions in processing speed.

Corpus Callosum Analysis using MDL-based Sequential Models of Shape and Appearance

This paper describes a method for automatically analysing and segmenting the corpus callosum from magnetic resonance images of the brain based on the widely used Active Appearance Models (AAMs) by Cootes et al. Extensions of the original method, which are designed to improve this specific case are proposed, but all remain applicable to other domain problems. The well-known multi-resolution AAM optimisation is extended to include sequential relaxations on texture resolution, model coverage and model parameter constraints. Fully unsupervised analysis is obtained by exploiting model parameter convergence limits and a maximum likelihood estimate of shape and pose. Further, the important problem of modelling object neighbourhood is addressed. Finally, we describe how correspondence across images is achieved by selecting the minimum description length (MDL) landmarks from a set of training boundaries using the recently proposed method of Davies et al. This MDL-approach ensures a unique parameterisation of corpus callosum contour variation, which is crucial for neurological studies that compare reference areas such as rostrum, splenium, et cetera. We present quantitative and qualitative results that show that the method produces accurate, robust and rapid segmentations in a cross sectional study of 17 subjects, establishing its feasibility as a fully automated clinical tool for analysis and segmentation.