Franziskus Liem

Sex beyond the genitalia: The human brain mosaic

Significance Sex/gender differences in the brain are of high social interest because their presence is typically assumed to prove that humans belong to two distinct categories not only in terms of their genitalia, and thus justify differential treatment of males and females. Here we show that, although there are sex/gender differences in brain and behavior, humans and human brains are comprised of unique “mosaics” of features, some more common in females compared with males, some more common in males compared with females, and some common in both females and males. Our results demonstrate that regardless of the cause of observed sex/gender differences in brain and behavior (nature or nurture), human brains cannot be categorized into two distinct classes: male brain/female brain. Whereas a categorical difference in the genitals has always been acknowledged, the question of how far these categories extend into human biology is still not resolved. Documented sex/gender differences in the brain are often taken as support of a sexually dimorphic view of human brains (“female brain” or “male brain”). However, such a distinction would be possible only if sex/gender differences in brain features were highly dimorphic (i.e., little overlap between the forms of these features in males and females) and internally consistent (i.e., a brain has only “male” or only “female” features). Here, analysis of MRIs of more than 1,400 human brains from four datasets reveals extensive overlap between the distributions of females and males for all gray matter, white matter, and connections assessed. Moreover, analyses of internal consistency reveal that brains with features that are consistently at one end of the “maleness-femaleness” continuum are rare. Rather, most brains are comprised of unique “mosaics” of features, some more common in females compared with males, some more common in males compared with females, and some common in both females and males. Our findings are robust across sample, age, type of MRI, and method of analysis. These findings are corroborated by a similar analysis of personality traits, attitudes, interests, and behaviors of more than 5,500 individuals, which reveals that internal consistency is extremely rare. Our study demonstrates that, although there are sex/gender differences in the brain, human brains do not belong to one of two distinct categories: male brain/female brain.

Cortical Surface Area and Cortical Thickness Demonstrate Differential Structural Asymmetry in Auditory-Related Areas of the Human Cortex

This investigation provides an analysis of structural asymmetries in 5 anatomically defined regions (Heschls gyrus, HG; Heschls sulcus, HS; planum temporale, PT; planum polare, PP; superior temporal gyrus, STG) within the human auditory-related cortex. Volumetric 3-dimensional T1-weighted magnetic resonance imaging scans were collected from 104 participants (52 males). Cortical volume (CV), cortical thickness (CT), and cortical surface area (CSA) were calculated based on individual scans of these anatomical traits. This investigation demonstrates a leftward asymmetry for CV and CSA that is observed in the HG, STG, and PT regions. As regards CT, we note a rightward asymmetry in the HG and HS. A correlation analysis of asymmetry indices between measurements for distinct regions of interest (ROIs) yields significant correlations between CT and CV in 4 of 5 ROIs (HG, HS, PT, and STG). Significant correlation values between CSA and CV are observed for all 5 ROIs. The findings suggest that auditory-related cortical areas demonstrate larger leftward asymmetry with respect to the CSA, while a clear rightward asymmetry with respect to CT is salient in both the primary and the secondary auditory cortex only. In addition, we propose that CV is not an ideal neuromarker for anatomical measurements. CT and CSA should be considered independent traits of anatomical asymmetries in the auditory-related cortex.

Brain Size, Sex, and the Aging Brain

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. Hum Brain Mapp, 36:150–169, 2015.

Longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging.

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. Hum Brain Mapp 35:4544–4555, 2014.

The hypothesis of neuronal interconnectivity as a function of brain size—a general organization principle of the human connectome

Twenty years ago, Ringo and colleagues proposed that maintaining absolute connectivity in larger compared with smaller brains is computationally inefficient due to increased conduction delays in transcallosal information transfer and expensive with respect to the brain mass needed to establish these additional connections. Therefore, they postulated that larger brains are relatively stronger connected intrahemispherically and smaller brains interhemispherically, resulting in stronger functional lateralization in larger brains. We investigated neuronal interconnections in 138 large and small human brains using diffusion tensor imaging-based fiber tractography. We found a significant interaction between brain size and the type of connectivity. Structural intrahemispheric connectivity is stronger in larger brains, whereas interhemispheric connectivity is only marginally increased in larger compared with smaller brains. Although brain size and gender are confounded, this effect is gender-independent. Additionally, the ratio of interhemispheric to intrahemispheric connectivity correlates inversely with brain size. The hypothesis of neuronal interconnectivity as a function of brain size might account for shorter and more symmetrical interhemispheric transfer times in women and for empirical evidence that visual and auditory processing are stronger lateralized in men. The hypothesis additionally shows that differences in interhemispheric and intrahemispheric connectivity are driven by brain size and not by gender, a finding contradicting a recently published study. Our findings are also compatible with the idea that the more asymmetric a region is, the smaller the density of interhemispheric connections, but the larger the density of intrahemispheric connections. The hypothesis represents an organization principle of the human connectome that might be applied also to non-human animals as suggested by our cross-species comparison.

Individual variation in intentionality in the mind-wandering state is reflected in the integration of the default-mode, fronto-parietal, and limbic networks

Abstract Mind‐wandering has a controversial relationship with cognitive control. Existing psychological evidence supports the hypothesis that episodes of mind‐wandering reflect a failure to constrain thinking to task‐relevant material, as well the apparently alternative view that control can facilitate the expression of self‐generated mental content. We assessed whether this apparent contradiction arises because of a failure to consider differences in the types of thoughts that occur during mind‐wandering, and in particular, the associated level of intentionality. Using multi‐modal magnetic resonance imaging (MRI) analysis, we examined the cortical organisation that underlies inter‐individual differences in descriptions of the spontaneous or deliberate nature of mind‐wandering. Cortical thickness, as well as functional connectivity analyses, implicated regions relevant to cognitive control and regions of the default‐mode network for individuals who reported high rates of deliberate mind‐wandering. In contrast, higher reports of spontaneous mind‐wandering were associated with cortical thinning in parietal and posterior temporal regions in the left hemisphere (which are important in the control of cognition and attention) as well as heightened connectivity between the intraparietal sulcus and a region that spanned limbic and default‐mode regions in the ventral inferior frontal gyrus. Finally, we observed a dissociation in the thickness of the retrosplenial cortex/lingual gyrus, with higher reports of spontaneous mind‐wandering being associated with thickening in the left hemisphere, and higher repots of deliberate mind‐wandering with thinning in the right hemisphere. These results suggest that the intentionality of the mind‐wandering state depends on integration between the control and default‐mode networks, with more deliberation being associated with greater integration between these systems. We conclude that one reason why mind‐wandering has a controversial relationship with control is because it depends on whether the thoughts emerge in a deliberate or spontaneous fashion. HighlightsDeliberate and spontaneous mind‐wandering have unique structural and functional correlates.Reports of deliberate mind‐wandering correlated with regions in both default‐mode and fronto‐parietal networks.Spontaneous mind‐wandering was linked to less integrity in parietal and temporal regions.Intentionality during the mind‐wandering state may depend upon integration between the default‐mode and fronto‐parietal networks.These neurocognitive differences explain why mind‐wandering has a complex relationship with cognitive control.

The “silent” imprint of musical training

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 Brocas 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.

Reliability and statistical power analysis of cortical and subcortical FreeSurfer metrics in a large sample of healthy elderly

FreeSurfer is a tool to quantify cortical and subcortical brain anatomy automatically and noninvasively. Previous studies have reported reliability and statistical power analyses in relatively small samples or only selected one aspect of brain anatomy. Here, we investigated reliability and statistical power of cortical thickness, surface area, volume, and the volume of subcortical structures in a large sample (N=189) of healthy elderly subjects (64+ years). Reliability (intraclass correlation coefficient) of cortical and subcortical parameters is generally high (cortical: ICCs>0.87, subcortical: ICCs>0.95). Surface-based smoothing increases reliability of cortical thickness maps, while it decreases reliability of cortical surface area and volume. Nevertheless, statistical power of all measures benefits from smoothing. When aiming to detect a 10% difference between groups, the number of subjects required to test effects with sufficient power over the entire cortex varies between cortical measures (cortical thickness: N=39, surface area: N=21, volume: N=81; 10mm smoothing, power=0.8, α=0.05). For subcortical regions this number is between 16 and 76 subjects, depending on the region. We also demonstrate the advantage of within-subject designs over between-subject designs. Furthermore, we publicly provide a tool that allows researchers to perform a priori power analysis and sensitivity analysis to help evaluate previously published studies and to design future studies with sufficient statistical power.

Predicting brain-age from multimodal imaging data captures cognitive impairment

Abstract The disparity between the chronological age of an individual and their brain‐age measured based on biological information has the potential to offer clinically relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain‐age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain‐imaging data improves age prediction. Using cortical anatomy and whole‐brain functional connectivity on a large adult lifespan sample (N=2354, age 19–82), we found that multimodal data improves brain‐based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain‐age measure was robust to confounding effects: head motion did not drive brain‐based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N=475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain‐age prediction to confounds, generalizability across sites, and sensitivity to clinically‐relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders. HighlightsBrain‐based age prediction is improved with multimodal neuroimaging data.Participants with cognitive impairment show increased brain aging.Age prediction models are robust to motion and generalize to independent datasets from other sites.

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.