R. Seiger

Structural Connectivity Networks of Transgender People

Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF) transgender patients before hormone therapy as compared with 25 female and 25 male healthy controls. Graph theoretical analysis of whole-brain probabilistic tractography networks (adjusted for differences in intracranial volume) showed decreased hemispheric connectivity ratios of subcortical/limbic areas for both transgender groups. Subsequent analysis revealed that this finding was driven by increased interhemispheric lobar connectivity weights (LCWs) in MtF transsexuals and decreased intrahemispheric LCWs in FtM patients. This was further reflected on a regional level, where the MtF group showed mostly increased local efficiencies and FtM patients decreased values. Importantly, these parameters separated each patient group from the remaining subjects for the majority of significant findings. This work complements previously established regional alterations with important findings of structural connectivity. Specifically, our data suggest that network parameters may reflect unique characteristics of transgender patients, whereas local physiological aspects have been shown to represent the transition from the biological sex to the actual gender identity.

Voxel-based morphometry at ultra-high fields. A comparison of 7 T and 3 T MRI data

Recent technological progress enables MRI recordings at ultra-high fields of 7 T and above leading to brain images of higher resolution and increased signal-to-noise ratio. Despite these benefits, imaging at 7 T exhibits distinct challenges due to B1 field inhomogeneities, causing decreased image quality and problems in data analysis. Although several strategies have been proposed, a systematic investigation of bias-corrected 7 T data for voxel-based morphometry (VBM) is still missing and it is an ongoing matter of debate if VBM at 7 T can be carried out properly. Here, an optimized VBM study was conducted, evaluating the impact of field strength (3T vs. 7 T) and pulse sequence (MPRAGE vs. MP2RAGE) on gray matter volume (GMV) estimates. More specifically, twenty-two participants were measured under the conditions 3T MPRAGE, 7 T MPRAGE and 7 T MP2RAGE. Due to the fact that 7 T MPRAGE data exhibited strong intensity inhomogeneities, an alternative preprocessing pipeline was proposed and applied for that data. VBM analysis revealed higher GMV estimates for 7 T predominantly in superior cortical areas, caudate nucleus, cingulate cortex and the hippocampus. On the other hand, 3T yielded higher estimates especially in inferior cortical areas of the brain, cerebellum, thalamus and putamen compared to 7 T. Besides minor exceptions, these results were observed for 7 T MPRAGE as well for the 7 T MP2RAGE measurements. Results gained in the inferior parts of the brain should be taken with caution, as native GM segmentations displayed misclassifications in these regions for both 7 T sequences. This was supported by the test-retest measurements showing highest variability in these inferior regions of the brain for 7 T and also for the advanced MP2RAGE sequence. Hence, our data support the use of 7 T MRI for VBM analysis in cortical areas, but direct comparison between field strengths and sequences requires careful assessment. Similarly, analysis of the inferior cortical regions, cerebellum and subcortical regions still remains challenging at 7 T even if the advanced MP2RAGE sequence is used.

Comparison of continuously acquired resting state and extracted analogues from active tasks

Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting‐state data, the application to task‐specific fMRI has received growing attention. Three major methods for extraction of resting‐state data from task‐related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in‐between task blocks. Despite widespread application in current research, consensus on which method best resembles resting‐state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting‐state, two different task paradigms were assessed (emotion discrimination and right finger‐tapping) and five well‐described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting‐state (Dice, Intraclass correlation coefficient (ICC), R2) showed that regression against task effects yields functional connectivity networks most alike to resting‐state. However, all methods exhibited significant differences when compared to continuous resting‐state and similarity metrics were lower than test‐retest of two resting‐state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting‐state when extracting signals from task designs, although functional connectivity computed from task‐specific data may indeed yield interesting information. Hum Brain Mapp 36:4053–4063, 2015.

Quantification of Task-Specific Glucose Metabolism with Constant Infusion of 18F-FDG

The investigation of cerebral metabolic rate of glucose (CMRGlu) at baseline and during specific tasks previously required separate scans with the drawback of high intrasubject variability. We aimed to validate a novel approach to assessing baseline glucose metabolism and task-specific changes in a single measurement with a constant infusion of 18F-FDG. Methods: Fifteen healthy subjects underwent two PET measurements with arterial blood sampling. As a reference, baseline CMRGlu was quantified from a 60-min scan after 18F-FDG bolus application using the Patlak plot (eyes closed). For the other scan, a constant radioligand infusion was applied for 95 min, during which the subjects opened their eyes at 10–20 min and 60–70 min and tapped their right thumb to their fingers at 35–45 min and 85–95 min. The constant-infusion scan was quantified in two steps. First, the general linear model was used to fit regional time–activity curves with regressors for baseline metabolism, task-specific changes for the eyes-open and finger-tapping conditions, and movement parameters. Second, the Patlak plot was used for quantification of CMRGlu. Multiplication of the baseline regressor by β-values from the general linear model yielded regionally specific time–activity curves for baseline metabolism. Further, task-specific changes in metabolism are directly proportional to changes in the slope of the time–activity curve and hence to changes in CMRGlu. Results: Baseline CMRGlu from the constant-infusion scan matched that from the bolus application (test–retest variability, 1.1% ± 24.7%), which was not the case for a previously suggested approach (variability, −39.9% ± 25.2%, P < 0.001). Task-specific CMRGlu increased in the primary visual and motor cortices for eyes open and finger tapping, respectively (P < 0.05, familywise error–corrected), with absolute changes of up to 2.1 μmol/100 g/min and 6.3% relative to baseline. For eyes open, a decreased CMRGlu was observed in default-mode regions (P < 0.05, familywise error–corrected). CMRGlu quantified with venous blood samples (n = 6) showed excellent agreement with results obtained from arterial samples (r > 0.99). Conclusion: Baseline glucose metabolism and task-specific changes can be quantified in a single measurement with constant infusion of 18F-FDG and venous blood sampling. The high sensitivity and regional specificity of the approach offer novel possibilities for functional and multimodal brain imaging.

Testosterone affects language areas of the adult human brain

Although the sex steroid hormone testosterone is integrally involved in the development of language processing, ethical considerations mostly limit investigations to single hormone administrations. To circumvent this issue we assessed the influence of continuous high‐dose hormone application in adult female‐to‐male transsexuals. Subjects underwent magnetic resonance imaging before and after 4 weeks of testosterone treatment, with each scan including structural, diffusion weighted and functional imaging. Voxel‐based morphometry analysis showed decreased gray matter volume with increasing levels of bioavailable testosterone exclusively in Brocas and Wernickes areas. Particularly, this may link known sex differences in language performance to the influence of testosterone on relevant brain regions. Using probabilistic tractography, we further observed that longitudinal changes in testosterone negatively predicted changes in mean diffusivity of the corresponding structural connection passing through the extreme capsule. Considering a related increase in myelin staining in rodents, this potentially reflects a strengthening of the fiber tract particularly involved in language comprehension. Finally, functional images at resting‐state were evaluated, showing increased functional connectivity between the two brain regions with increasing testosterone levels. These findings suggest testosterone‐dependent neuroplastic adaptations in adulthood within language‐specific brain regions and connections. Importantly, deteriorations in gray matter volume seem to be compensated by enhancement of corresponding structural and functional connectivity. Hum Brain Mapp 37:1738–1748, 2016.

Subcortical gray matter changes in transgender subjects after long-term cross-sex hormone administration

Sex-steroid hormones are primarily involved in sexual differentiation and development and are thought to underlie processes related to cognition and emotion. However, divergent results have been reported concerning the effects of hormone administration on brain structure including side effects like brain atrophy and dementia. Cross-sex hormone therapy in transgender subjects offers a unique model for studying the effects of sex hormones on the living human brain. In this study, 25 Female-to-Male (FtM) and 14 Male-to-Female (MtF) subjects underwent MRI examinations at baseline and after a period of at least 4-months of continuous cross-sex hormone administration. While MtFs received estradiol and anti-androgens, FtM subjects underwent high-dose testosterone treatment. The longitudinal processing stream of the FreeSurfer software suite was used for the automated assessment and delineation of brain volumes to assess the structural changes over the treatment period of cross-sex hormone administration. Most prominent results were found for MtFs receiving estradiol and anti-androgens in the form of significant decreases in the hippocampal region. Further analysis revealed that these decreases were reflected by increases in the ventricles. Additionally, changes in progesterone levels correlated with changes in gray matter structures in MtF subjects. In line with prior studies, our results indicate hormonal influences on subcortical structures related to memory and emotional processing. Additionally, this study adds valuable knowledge that progesterone may play an important role in this process.

Gender transition affects neural correlates of empathy: A resting state functional connectivity study with ultra high-field 7T MR imaging.

Sex-steroid hormones have repeatedly been shown to influence empathy, which is in turn reflected in resting state functional connectivity (rsFC). Cross-sex hormone treatment in transgender individuals provides the opportunity to examine changes to rsFC over gender transition. We aimed to investigate whether sex-steroid hormones influence rsFC patterns related to unique aspects of empathy, namely emotion recognition and description as well as emotional contagion. RsFC data was acquired with 7Tesla magnetic resonance imaging in 24 male-to-female (MtF) and 33 female-to-male (FtM) transgender individuals before treatment, in addition to 33 male- and 44 female controls. Of the transgender participants, 15 MtF and 20 FtM were additionally assessed after 4 weeks and 4 months of treatment. Empathy scores were acquired at the same time-points. MtF differed at baseline from all other groups and assimilated over the course of gender transition in a rsFC network around the supramarginal gyrus, a region central to interpersonal emotion processing. While changes to sex-steroid hormones did not correlate with rsFC in this network, a sex hormone independent association between empathy scores and rsFC was found. Our results underline that 1) MtF transgender persons demonstrate unique rsFC patterns in a network related to empathy and 2) changes within this network over gender transition are likely related to changes in emotion recognition, -description, and -contagion, and are sex-steroid hormone independent.

Effects of sex hormone treatment on white matter microstructure in individuals with gender dysphoria

Abstract Sex steroid hormones such as estradiol and testosterone are known to have organizing, as well as activating effects on neural tissue in animals and humans. This study investigated the effects of transgender hormone replacement therapy on white matter microstructure using diffusion tensor imaging. Female‐to‐male and male‐to‐female transgender participants were measured at baseline, four weeks and four months past treatment start and compared to female and male controls. We observed androgenization‐related reductions in mean diffusivity and increases in fractional anisotropy. We also observed feminization‐related increases in mean diffusivity and reductions in fractional anisotropy. In both transgender participants and controls, hormonal fluctuations were correlated with changes in white matter microstructure. Although the present study does not preclude regression to the mean as a potential contributing factor, the results indicate that sex hormones are – at least in part – responsible for white matter variability in the human brain. Studies investigating the effects of sex hormones on adult human brain structure may be an important route for greater understanding of the psychological differences between females and males. HighlightsEffects of sex hormone treatment on white matter microstructure was investigated.Female‐to‐male and Male‐to‐female transgender people and controls were included.Androgenization reduced mean diffusivity and increased fractional anisotropy.Feminization increased mean diffusivity and reduced fractional anisotropy.

Spatial analysis and high resolution mapping of the human whole-brain transcriptome for integrative analysis in neuroimaging

ABSTRACT The quantification of big pools of diverse molecules provides important insights on brain function, but is often restricted to a limited number of observations, which impairs integration with other modalities. To resolve this issue, a method allowing for the prediction of mRNA expression in the entire brain based on microarray data provided in the Allen Human Brain Atlas was developed. Microarray data of 3702 samples from 6 brain donors was registered to MNI and cortical surface space using FreeSurfer. For each of 18,686 genes, spatial dependence of transcription was assessed using variogram modelling. Variogram models were employed in Gaussian process regression to calculate best linear unbiased predictions for gene expression at all locations represented in well‐established imaging atlases for cortex, subcortical structures and cerebellum. For validation, predicted whole‐brain transcription of the HTR1A gene was correlated with [carbonyl‐11C]WAY‐100635 positron emission tomography data collected from 30 healthy subjects. Prediction results showed minimal bias ranging within ±0.016 (cortical surface), ±0.12 (subcortical regions) and ±0.14 (cerebellum) in units of log2 expression intensity for all genes. Across genes, the correlation of predicted and observed mRNA expression in leave‐one‐out cross‐validation correlated with the strength of spatial dependence (cortical surface: r=0.91, subcortical regions: r=0.85, cerebellum: r=0.84). 816 out of 18,686 genes exhibited a high spatial dependence accounting for more than 50% of variance in the difference of gene expression on the cortical surface. In subcortical regions and cerebellum, different sets of genes were implicated by high spatially structured variability. For the serotonin 1A receptor, correlation between PET binding potentials and predicted comprehensive mRNA expression was markedly higher (Spearman &rgr;=0.72 for cortical surface, &rgr;=0.84 for subcortical regions) than correlation of PET and discrete samples only (&rgr;=0.55 and &rgr;=0.63, respectively). Prediction of mRNA expression in the entire human brain allows for intuitive visualization of gene transcription and seamless integration in multimodal analysis without bias arising from non‐uniform distribution of available samples. Extension of this methodology promises to facilitate translation of omics research and enable investigation of human brain function at a systems level. HIGHLIGHTComprehensive mRNA expression atlases in MNI and surface space for each gene.Gaussian process regression corrects bias from non‐uniform distribution of samples.Improved correlation with PET data shown for the serotonin 1A receptor.Models of spatial dependence vary across brain structures for each gene.High spatially structured variability indicates relevant topology of transcription.

Cortical Thickness Estimations of FreeSurfer and the CAT12 Toolbox in Patients with Alzheimer's Disease and Healthy Controls: Cortical Thickness of FreeSurfer and CAT12

Automated cortical thickness (CT) measurements are often used to assess gray matter changes in the healthy and diseased human brain. The FreeSurfer software is frequently applied for this type of analysis. The computational anatomy toolbox (CAT12) for SPM, which offers a fast and easy‐to‐use alternative approach, was recently made available.