This study was conducted to examine the statistical influence of brain size on cortical, subcortical, and cerebellar compartmental volumes. This brain size influence was especially studied to delineate interactions with Sex and Age. Here, we studied 856 healthy subjects of which 533 are classified as young and 323 as old. Using an automated segmentation procedure cortical (gray and white matter [GM and WM] including the corpus callosum), cerebellar (GM and WM), and subcortical (thalamus, putamen, pallidum, caudatus, hippocampus, amygdala, and accumbens) volumes were measured and subjected to statistical analyses. These analyses revealed that brain size and age exert substantial statistical influences on nearly all compartmental volumes. Analyzing the raw compartmental volumes replicated the frequently reported Sex differences in compartmental volumes with men showing larger volumes. However, when statistically controlling for brain size Sex differences and Sex × Age interactions practically disappear. Thus, brain size is more important than Sex in explaining interindividual differences in compartmental volumes. The influence of brain size is discussed in the context of an allometric scaling of the compartmental volumes.

Previous neuroimaging studies in the field of motor learning have shown that learning a new skill induces specific changes of neural gray and white matter in human brain areas necessary to control the practiced task. Former longitudinal studies investigating motor skill learning have used strict training protocols with little ecological validity rather than physical leisure activities, although there are several retrospective and cross-sectional studies suggesting neuroprotective effects of physical leisure activities. In the present longitudinal MRI study, we used voxel-based morphometry to investigate training-induced gray matter changes in golf novices between the age of 40 and 60 years, an age period when an active life style is assumed to counteract cognitive decline. As a main result, we demonstrate that 40 h of golf practice, performed as a leisure activity with highly individual training protocols, are associated with gray matter increases in a task-relevant cortical network encompassing sensorimotor regions and areas belonging to the dorsal stream. A new and striking result is the relationship between training intensity (time needed to complete the 40 training hours) and structural changes observed in the parieto-occipital junction. Thus, we demonstrate that a physical leisure activity induces training-dependent changes in gray matter and assume that a strict and controlled training protocol is not mandatory for training-induced adaptations of gray matter.

Little is still known about the neuroanatomical substrates related to changes in specific cognitive abilities in the course of healthy aging, and the existing evidence is predominantly based on cross-sectional studies. However, to understand the intricate dynamics between developmental changes in brain structure and changes in cognitive ability, longitudinal studies are needed. In the present article, we review the current longitudinal evidence on correlated changes between magnetic resonance imaging-derived measures of brain structure (e.g. gray matter/white matter volume, cortical thickness), and laboratory-based measures of fluid cognitive ability (e.g. intelligence, memory, processing speed) in healthy older adults. To theoretically embed the discussion, we refer to the revised Scaffolding Theory of Aging and Cognition. We found 31 eligible articles, with sample sizes ranging from n = 25 to n = 731 (median n = 104), and participant age ranging from 19 to 103. Several of these studies report positive correlated changes for specific regions and specific cognitive abilities (e.g. between structures of the medial temporal lobe and episodic memory). However, the number of studies presenting converging evidence is small, and the large methodological variability between studies precludes general conclusions. Methodological and theoretical limitations are discussed. Clearly, more empirical evidence is needed to advance the field. Therefore, we provide guidance for future researchers by presenting ideas to stimulate theory and methods for development.

In this study, we examined whether age can be predicted on the basis of different anatomical features obtained from a large sample of healthy subjects (n = 3,144). From this sample we obtained different anatomical feature sets: (1) 11 larger brain regions (including cortical volume, thickness, area, subcortical volume, cerebellar volume, etc.), (2) 148 cortical compartmental thickness measures, (3) 148 cortical compartmental area measures, (4) 148 cortical compartmental volume measures, and (5) a combination of the above-mentioned measures. With these anatomical feature sets, we predicted age using 6 statistical techniques (multiple linear regression, ridge regression, neural network, k-nearest neighbourhood, support vector machine, and random forest). We obtained very good age prediction accuracies, with the highest accuracy being R = 0.84 (prediction on the basis of a neural network and support vector machine approaches for the entire data set) and the lowest being R = 0.40 (prediction on the basis of a k-nearest neighborhood for cortical surface measures). Interestingly, the easy-to-calculate multiple linear regression approach with the 11 large brain compartments resulted in a very good prediction accuracy (R = 0.73), whereas the application of the neural network approach for this data set revealed very good age prediction accuracy (R = 0.83). Taken together, these results demonstrate that age can be predicted well on the basis of anatomical measures. The neural network approach turned out to be the approach with the best results. In addition, it was evident that good prediction accuracies can be achieved using a small but nevertheless age-representative dataset of brain features. Hum Brain Mapp 38:997-1008, 2017. © 2016 Wiley Periodicals, Inc.

The ability to accurately process numerical magnitudes and solve mental arithmetic is of highest importance for schooling and professional career. Although impairments in these domains in disorders such as developmental dyscalculia (DD) are highly detrimental, remediation is still sparse. In recent years, transcranial brain stimulation methods such as transcranial Direct Current Stimulation (tDCS) have been suggested as a treatment for various neurologic and neuropsychiatric disorders. The posterior parietal cortex (PPC) is known to be crucially involved in numerical magnitude processing and mental arithmetic. In this study, we evaluated whether tDCS has a beneficial effect on numerical magnitude processing and mental arithmetic. Due to the unclear lateralization, we stimulated the left, right as well as both hemispheres simultaneously in two experiments. We found that left anodal tDCS significantly enhanced performance in a number comparison and a subtraction task, while bilateral and right anodal tDCS did not induce any improvements compared to sham. Our findings demonstrate that the left PPC is causally involved in numerical magnitude processing and mental arithmetic. Furthermore, we show that these cognitive functions can be enhanced by means of tDCS. These findings encourage to further investigate the beneficial effect of tDCS in the domain of mathematics in healthy and impaired humans.

BackgroundVirtual reality (VR) offers powerful therapy options within a functional, purposeful and motivating context. Several studies have shown that patients' motivation plays a crucial role in determining therapy outcome. However, few studies have demonstrated the potential of VR in pediatric rehabilitation. Therefore, we developed a VR-based soccer scenario, which provided interactive elements to engage patients during robotic assisted treadmill training (RAGT). The aim of this study was to compare the immediate effect of different supportive conditions (VR versus non-VR conditions) on motor output in patients and healthy control children during training with the driven gait orthosis Lokomat®.MethodsA total of 18 children (ten patients with different neurological gait disorders, eight healthy controls) took part in this study. They were instructed to walk on the Lokomat in four different, randomly-presented conditions: (1) walk normally without supporting assistance, (2) with therapists' instructions to promote active participation, (3) with VR as a motivating tool to walk actively and (4) with the VR tool combined with therapists' instructions. The Lokomat gait orthosis is equipped with sensors at hip and knee joint to measure man-machine interaction forces. Additionally, subjects' acceptance of the RAGT with VR was assessed using a questionnaire.ResultsThe mixed ANOVA revealed significant main effects for the factor CONDITIONS (p < 0.001) and a significant interaction CONDITIONS × GROUP (p = 0.01). Tests of between-subjects effects showed no significant main effect for the GROUP (p = 0.592). Active participation in patients and control children increased significantly when supported and motivated either by therapists' instructions or by a VR scenario compared with the baseline measurement "normal walking" (p < 0.001).ConclusionsThe VR scenario used here induces an immediate effect on motor output to a similar degree as the effect resulting from verbal instructions by the therapists. Further research needs to focus on the implementation of interactive design elements, which keep motivation high across and beyond RAGT sessions, especially in pediatric rehabilitation.

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Relatively little is known about reliability of longitudinal diffusion-tensor imaging (DTI) measurements despite growing interest in using DTI to track change in white matter structure. The purpose of this study is to quantify within- and between session scan-rescan reliability of DTI-derived measures that are commonly used to describe the characteristics of neural white matter in the context of neural plasticity research. DTI data were acquired from 16 cognitively healthy older adults (mean age 68.4). We used the Tract-Based Spatial Statistics (TBSS) approach implemented in FSL, evaluating how different DTI preprocessing choices affect reliability indices. Test-Retest reliability, quantified as ICC averaged across the voxels of the TBSS skeleton, ranged from 0.524 to 0.798 depending on the specific DTI-derived measure and the applied preprocessing steps. The two main preprocessing steps that we found to improve TBSS reliability were (a) the use of a common individual template and (b) smoothing DTI data using a 1-voxel median filter. Overall our data indicate that small choices in the preprocessing pipeline have a significant effect on test-retest reliability, therefore influencing the power to detect change within a longitudinal study. Furthermore, differences in the data processing pipeline limit the comparability of results across studies.