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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 2  = 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 2  = 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 2  = 0.73), whereas the application of the neural network approach for this data set revealed very good age prediction accuracy (R 2  = 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., (© 2016 Wiley Periodicals, Inc.)

Playing a musical instrument at a professional level is a complex multimodal task requiring information integration between different brain regions supporting auditory, somatosensory, motor, and cognitive functions. These kinds of task-specific activations are known to have a profound influence on both the functional and structural architecture of the human brain. However, until now, it is widely unknown whether this specific imprint of musical practice can still be detected during rest when no musical instrument is used. Therefore, we applied high-density electroencephalography and evaluated whole-brain functional connectivity as well as small-world topologies (i.e., node degree) during resting state in a sample of 15 professional musicians and 15 nonmusicians. As expected, musicians demonstrate increased intra- and interhemispheric functional connectivity between those brain regions that are typically involved in music perception and production, such as the auditory, the sensorimotor, and prefrontal cortex as well as Broca's area. In addition, mean connectivity within this specific network was positively related to musical skill and the total number of training hours. Thus, we conclude that musical training distinctively shapes intrinsic functional network characteristics in such a manner that its signature can still be detected during a task-free condition. Hum Brain Mapp 37:536-546, 2016. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)

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., (© 2014 Wiley Periodicals, Inc.)

Social anxiety disorder (SAD) is the second leading anxiety disorder. On the functional neurobiological level, specific brain regions involved in the processing of anxiety-laden stimuli and in emotion regulation have been shown to be hyperactive and hyper-responsive in SAD such as amygdala, insula and orbito- and prefrontal cortex. On the level of brain structure, prior studies on anatomical differences in SAD resulted in mixed and partially contradictory findings. Based on previous functional and anatomical models of SAD, this study examined cortical thickness in structural magnetic resonance imaging data of 46 patients with SAD without comorbidities (except for depressed episode in one patient) compared with 46 matched healthy controls in a region of interest-analysis and in whole-brain. In a theory-driven ROI-analysis, cortical thickness was increased in SAD in left insula, right anterior cingulate and right temporal pole. Furthermore, the whole-brain analysis revealed increased thickness in right dorsolateral prefrontal and right parietal cortex. This study detected no regions of decreased cortical thickness or brain volume in SAD. From the perspective of brain networks, these findings are in line with prior functional differences in salience networks and frontoparietal networks associated with executive-controlling and attentional functions., (Copyright © 2013 Wiley Periodicals, Inc.)

Investigation of the brain's white matter fiber tracts in social anxiety disorder (SAD) may provide insight into the underlying pathophysiology. Because models of pathological anxiety posit altered frontolimbic interactions, the uncinate fasciculus (UF) connecting (orbito-) frontal and temporal areas including the amygdala is of particular interest. Microstructural alterations in parts of the UF have been reported previously, whereas examination of the UF as discrete fiber tract with regard to more large-scale properties is still lacking. Diffusion tensor imaging was applied in 25 patients with generalized SAD and 25 healthy control subjects matched by age and gender. By means of fiber tractography, the UF was reconstructed for each participant. The inferior fronto-occipital fasciculus (IFOF), originating from the frontal cortex similarly to the UF, was additionally included as control tract. Volume and fractional anisotropy (FA) were compared between the groups for both tracts. Volume of left and right UF was reduced in patients with SAD, reaching statistical significance for the left UF. Bilateral IFOF volume was not different between groups. A similar pattern was observed for FA. Reduced volume of the left UF in SAD fits well into pathophysiological models of anxiety, as it suggests deficient structural connectivity between higher-level control areas in the orbitofrontal cortex and more basal limbic areas like the amygdala. The results point to a specific role of the left UF with regard to altered white matter volume in SAD. However, results should be replicated and functional correlates of altered UF volume be determined in future studies., (Copyright © 2011 Wiley Periodicals, Inc.)

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions., (Copyright © 2011 Wiley-Liss, Inc.)

The technique of diffusion tensor imaging (DTI) has been used to investigate alterations in white matter architecture following long-term training and expertise. Professional simultaneous interpreters (SI) provide an ideal model for the investigation of training-induced plasticity due to the high demands placed on sound to motor mapping mechanisms, which are vital for executing fast interpretations. In line with our hypothesis, we found clusters with decreased fractional anisotropy (FA) in the SI group in brain regions previously shown to support sensory-motor coupling mechanisms and speech articulation (cluster extent family-wise error corrected, P < 0.01). Furthermore, we found an altered white matter architecture indicated by lower FA values in the SI group in the most anterior and posterior parts of the corpus callosum. Our results suggest that language expertise is accompanied by plastic adaptations in regions strongly involved in motor aspects of speech and in interhemispheric information transfer. These results have implications for our understanding of language expertise in relation to white matter adaptations., (Copyright © 2010 Wiley Periodicals, Inc.)

Evidence suggests that motor, sensory, and cognitive training modulates brain structures involved in a specific practice. Functional neuroimaging revealed key brain structures involved in dancing such as the putamen and the premotor cortex. Intensive ballet dance training was expected to modulate the structures of the sensorimotor network, for example, the putamen, premotor cortex, supplementary motor area (SMA), and the corticospinal tracts. We investigated gray (GM) and white matter (WM) volumes, fractional anisotropy (FA), and mean diffusivity (MD) using magnetic resonance-based morphometry and diffusion tensor imaging in 10 professional female ballet dancers compared with 10 nondancers. In dancers compared with nondancers, decreased GM volumes were observed in the left premotor cortex, SMA, putamen, and superior frontal gyrus, and decreased WM volumes in both corticospinal tracts, both internal capsules, corpus callosum, and left anterior cingulum. FA was lower in the WM underlying the dancers' left and right premotor cortex. There were no significant differences in MD between the groups. Age of dance commencement was negatively correlated with GM and WM volume in the right premotor cortex and internal capsule, respectively, and positively correlated with WM volume in the left precentral gyrus and corpus callosum. Results were not influenced by the significantly lower body mass index of the dancers. The present findings complement the results of functional imaging studies in experts that revealed reduced neural activity in skilled compared with nonskilled subjects. Reductions in brain activity are accompanied by local decreases in GM and WM volumes and decreased FA., (2009 Wiley-Liss, Inc.)

Background and Purpose: McLeod neuroacanthocytosis syndrome (MLS) is an X-linked multisystem disorder with CNS manifestations resembling Huntington disease. Neuroimaging studies revealed striatal atrophy with predominance of the caudate nucleus. Our previous cross-sectional MRI study showed an association of volume loss in the caudate nucleus and putamen with the disease duration., Methods: In the present study, we examined three brothers with genetically confirmed diagnosis of MLS using an observer-independent and fully automated subcortical segmentation procedure to measure striatal volumes., Results: In a cross-sectional comparison with 20 healthy age-matched control men, the volumes of the caudate nucleus of the three patients were significantly smaller as confirmed by z-score transformations. On an individual basis, volumes in the two more severely affected and older patients were smaller than in the less affected younger brother. Longitudinal MRI-based measurements over 7 years demonstrated a statistical trend towards significant decreased caudate volumes in McLeod patients., Conclusions: Our findings indicate that structural MRI combined with fully automated computational morphometric analyses represents an objective and observer-independent imaging tool for the representation of progressive striatal degeneration in MLS and might be a valuable methodology for cross-sectional as well as longitudinally volumetric studies in other rare neurodegenerative diseases, even on individual patients.