A. Komorowski

Association of Protein Distribution and Gene Expression Revealed by PET and Post-Mortem Quantification in the Serotonergic System of the Human Brain.

Abstract Regional differences in posttranscriptional mechanisms may influence in vivo protein densities. The association of positron emission tomography (PET) imaging data from 112 healthy controls and gene expression values from the Allen Human Brain Atlas, based on post‐mortem brains, was investigated for key serotonergic proteins. PET binding values and gene expression intensities were correlated for the main inhibitory (5‐HT1A) and excitatory (5‐HT2A) serotonin receptor, the serotonin transporter (SERT) as well as monoamine oxidase‐A (MAO‐A), using Spearmans correlation coefficients (rs) in a voxel‐wise and region‐wise analysis. Correlations indicated a strong linear relationship between gene and protein expression for both the 5‐HT1A (voxel‐wise rs = 0.71; region‐wise rs = 0.93) and the 5‐HT2A receptor (rs = 0.66; 0.75), but only a weak association for MAO‐A (rs = 0.26; 0.66) and no clear correlation for SERT (rs = 0.17; 0.29). Additionally, region‐wise correlations were performed using mRNA expression from the HBT, yielding comparable results (5‐HT1Ars = 0.82; 5‐HT2Ars = 0.88; MAO‐A rs = 0.50; SERT rs = −0.01). The SERT and MAO‐A appear to be regulated in a region‐specific manner across the whole brain. In contrast, the serotonin‐1A and ‐2A receptors are presumably targeted by common posttranscriptional processes similar in all brain areas suggesting the applicability of mRNA expression as surrogate parameter for density of these proteins.

7T imaging of the wrist.

Ultra-high-field magnetic resonance (MR) imaging provides a high signal-to-noise ratio and thus is expected to be superior to 1.5T and 3T MR systems that are currently used in daily routine. For use in the musculoskeletal system, expectations are high, particularly for smaller joints such as the wrist, because of the small size of the visualized anatomical structures, where high spatial resolution imaging is mandatory. However, there are technical challenges associated with ultra-high-field MR, and much of the necessary basic research has been done. This article reviews the literature of the past 10 years of research in this field, which reveals a promising pattern of continuing improvements and further developments. For this reason, it is likely that, in the near future, studies with larger study populations and more clinically driven research questions will follow.

Brain monoamine oxidase A in seasonal affective disorder and treatment with bright light therapy

Increased cerebral monoamine oxidase A (MAO-A) levels have been shown in non-seasonal depression using positron emission tomography (PET). Seasonal affective disorder (SAD) is a sub-form of major depressive disorder and is typically treated with bright light therapy (BLT). The serotonergic system is affected by season and light. Hence, this study aims to assess the relevance of brain MAO-A levels to the pathophysiology and treatment of SAD. Changes to cerebral MAO-A distribution (1) in SAD in comparison to healthy controls (HC), (2) after treatment with BLT and (3) between the seasons, were investigated in 24 patients with SAD and 27 HC using [11C]harmine PET. PET scans were performed in fall/winter before and after 3 weeks of placebo-controlled BLT, as well as in spring/summer. Cerebral MAO-A distribution volume (VT, an index of MAO-A density) did not differ between patients and HC at any of the three time-points. However, MAO-A VT decreased from fall/winter to spring/summer in the HC group (F1, 187.84 = 4.79, p < 0.050), while SAD showed no change. In addition, BLT, but not placebo, resulted in a significant reduction in MAO-A VT (F1, 208.92 = 25.96, p < 0.001). This is the first study to demonstrate an influence of BLT on human cerebral MAO-A levels in vivo. Furthermore, we show that SAD may lack seasonal dynamics in brain MAO-A levels. The lack of a cross-sectional difference between patients and HC, in contrast to studies in non-seasonal depression, may be due to the milder symptoms typically shown by patients with SAD.

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.

Correction to: The pulvinar nucleus and antidepressant treatment: dynamic modeling of antidepressant response and remission with ultra-high field functional MRI

The author list was presented as last name, first name. The names should have been listed as:Christoph Kraus, Manfred Klöbl, Martin Tik, Bastian Auer, Thomas Vanicek, Nicole Geissberger, Daniela M. Pfabigan, Andreas Hahn, Michael Woletz, Katharina Paul, Arkadiusz Komorowski, Siegfried Kasper, Christian Windischberger, Claus Lamm, Rupert Lanzenberger.