[Show abstract][Hide abstract] ABSTRACT: In a population-based study of 1,912 community-dwelling persons of 45 years and older, we investigated the relation between age and fine motor skills using the Archimedes spiral-drawing test. Also, we studied the effect of brain volume on fine motor skills.
Frontiers in Aging Neuroscience 09/2014; 6:259. · 2.84 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: It is unknown whether the cerebellum affects cognitive function in an aging community-dwelling population. In a population-based study on 3745 nondemented individuals aged 45 years and above, we investigated the relationship between cerebellar volume and cognitive function.
Brain volumes were obtained using automatic tissue segmentation of magnetic resonance imaging scans. Cognitive functioning was assessed using MMSE and cognitive compound scores of global cognition, executive function, information processing speed, memory, and motor speed. Linear regression modeling was used to study the associations between cerebellar volumes and cognitive measures, independent of cerebral volumes.
We found a relationship between larger cerebellar volume and better global cognition, executive function, information processing speed, and motor speed. After adjustment for cerebral volume, only cerebellar gray matter volume remained borderline significantly associated with global cognition and information processing speed. After Bonferroni correction, the few associations found between cerebellar volume and cognition disappeared.
We only found a minor relationship between larger cerebellar volume and better cognition in healthy older adults, which further attenuated after correcting for cerebral volume. Our findings support the notion that cerebellar volume has an influence on cognition in aging, but that it is not the major leading structure.
Alzheimer disease and associated disorders 02/2014; · 2.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Cognitive impairment is an important hallmark of dementia, but deterioration of cognition also occurs frequently in non-demented elderly individuals. In more than 3,000 non-demented persons, aged 45-99 years, from the population-based Rotterdam Study we studied cross-sectional age effects on cognitive function across various domains. All participants underwent an extensive cognitive test battery that tapped into processing speed, executive function, verbal fluency, verbal recall and recognition, visuospatial ability and fine motor skills. General cognitive function was assessed by the g-factor, which was derived from principal component analysis and captured 49.2 % of all variance in cognition. We found strongest associations for age with g-factor [difference in z-score -0.59 per 10 years; 95 % confidence interval (CI) -0.62 to -0.56], fine motor skill (-0.53 per 10 years; 95 % CI -0.56 to -0.50), processing speed (-0.49 per 10 years; 95 % CI -0.51 to -0.46), and visuospatial ability (-0.48 per 10 years; 95 % CI -0.51 to -0.45). In contrast, the effect size for the association between age and immediate recall was only -0.25 per 10 years (95 % CI -0.28 to -0.22), which was significantly smaller than the relation between age and fine motor skill (P < 0.001). In conclusion, in non-demented persons of 45 years and older, general cognition deteriorates with aging. More specifically, fine motor skill, processing speed and visuospatial ability, but not memory, are affected most by age.
European Journal of Epidemiology 02/2014; · 5.15 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Poor gait is an important risk factor for falls and associated with higher morbidity and mortality. It is well established that older age is associated with worse gait, but it remains unclear at what age this association is first seen. Moreover, previous studies focused mainly on normal walking, but gait also encompasses turning and tandem walking. In a large study of community-dwelling middle-aged and elderly persons we investigated the association of age with gait, focusing on normal walking, turning and tandem walking. In 1500 persons aged 50 years and over, we measured gait using an electronic walkway. Participants performed normal walks, turning and a tandem walk. With principal components analysis of 30 variables we summarized gait into five known gait factors: Rhythm, Variability, Phases, Pace and Base of Support; and uncovered two novel gait factors: Tandem and Turning. The strongest associations with age were found for Variability (difference in Z-score -0.29 per 10 years increase (95% confidence interval: -0.34; -0.24)), Phases (-0.31 per 10 years (-0.36; -0.27)) and Tandem (-0.25 per 10 years (-0.30; -0.20)). Additionally, these factors already showed association with the youngest age groups, from 55 to 60 years of age and older. Our study shows that Variability, Phases and Tandem have the strongest association with age and are the earliest to demonstrate a poorer gait pattern with higher age. Future research should further investigate how these gait factors relate with gait-related diseases in their earliest stages.
[Show abstract][Hide abstract] ABSTRACT: In a population-based study of 3962 community-dwelling nondemented elderly we investigated the relation of age, sex, cardiovascular risk factors, and the presence of infarcts with cerebellar volume, and its interrelationship with cerebral volumes. Cerebellar and cerebral gray and white matter were segmented using Freesurfer version 4.5 (http://surfer.nmr.mgh.harvard.edu/). We used linear regression analyses to model the relationship between age, sex, cardiovascular risk factors, brain infarcts, white matter lesions (WMLs) and cerebellar and cerebral volume. Smaller cerebellar volumes with increasing age were mainly driven by loss of white matter. Diabetes, higher serum glucose and lower cholesterol levels were related to smaller cerebellar volume. No association was found between hypertension, smoking, apolipoprotein E (ApoE) genotype, and cerebellar volume. Supratentorial lacunar infarcts and WMLs were related to smaller cerebellar volume. Infratentorial infarcts were related to smaller cerebellar white matter volume and total cerebral volume. This study suggests that determinants of cerebellar volume do not entirely overlap with those established for cerebral volume. Furthermore, presence of infarcts or WMLs in the cerebrum can affect cerebellar volume.
Neurobiology of aging 03/2012; 33(12):2774-81. · 5.94 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Accurate automated brain structure segmentation methods facilitate the analysis of large-scale neuroimaging studies. This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information about a structure's location and appearance. The spatial model is implemented by registering multiple atlas images to the target image and creating a spatial probability map. The structure's appearance is modeled by a classifier based on Gaussian scale-space features. These components are combined with a regularization term in a Bayesian framework that is globally optimized using graph cuts. The incorporation of the appearance model enables the method to segment structures with complex intensity distributions and increases its robustness against errors in the spatial model. The method is tested in cross-validation experiments on two datasets acquired with different magnetic resonance sequences, in which the hippocampus and cerebellum were segmented by an expert. Furthermore, the method is compared to two other segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces accurate results with mean Dice similarity indices of 0.95 for the cerebellum, and 0.87 for the hippocampus. This was comparable to or better than the other methods, whereas the proposed technique is more widely applicable and robust.
IEEE transactions on medical imaging. 09/2011; 31(2):276-86.
[Show abstract][Hide abstract] ABSTRACT: We propose a novel cerebellum segmentation method for MRI, based on a combination of statistical models of the structure's expected location in the brain and its local appearance. The appearance model is obtained from a k-nearest-neighbor classifier, which uses a set of multi-scale local image descriptors as features. The spatial model is constructed by registering multiple manually annotated datasets to the unlabeled target image. The two components are then combined in a Bayesian framework. The method is quantitatively validated in a leave-one-out experiment using 18 MR images of elderly subjects. The experiment showed that the method produces accurate segmentations. The mean Dice similarity index compared to the manual reference was 0.953 for left and right, and the mean surface distance was 0.49 mm for left and 0.50 mm for right. The combined atlas- and appearance-based method was found to be more accurate than a method based on atlas-registration alone.
Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009; 01/2009