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Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the human brain and plays a key role in several brain functions and neuropsychiatric disorders such as anxiety, epilepsy, and depression. For decades, several in vivo and ex vivo techniques have been used to highlight the mechanisms of the GABA system, however, no studies have currently combined the techniques to create a high-resolution multimodal view of the GABA system. Here, we present a quantitative high-resolution in vivo atlas of the human brain benzodiazepine receptor sites (BZR) located on postsynaptic ionotropic GABAA receptors (GABAARs), generated on the basis of in vivo [11C]flumazenil Positron Emission Tomography (PET) data. Next, based on ex vivo autoradiography data, we transform the PET-generated atlas from binding values into BZR protein density. Finally, we examine the brain regional association between BZR protein density and ex vivo mRNA expression for the 19 subunits in the GABAAR, including an estimation of the minimally required expression of mRNA levels for each subunit to translate into BZR protein. This represents the first publicly available quantitative high-resolution in vivo atlas of the spatial distribution of BZR densities in the healthy human brain. The atlas provides a unique neuroscientific tool as well as novel insights into the association between mRNA expression for individual subunits in the GABAAR and the BZR density at each location in the brain.

The human brain atlas of the serotonin (5-HT) system does not conform with commonly used parcellations of neocortex, since the spatial distribution of homogeneous 5-HT receptors and transporter is not aligned with such brain regions. This discrepancy indicates that a neocortical parcellation specific to the 5-HT system is needed. We first outline issues with an existing parcellation of the 5-HT system, and present an alternative parcellation derived from brain MR- and high-resolution PET images of five different 5-HT targets from 210 healthy controls. We then explore how well this new 5-HT parcellation can explain mRNA levels of all 5-HT genes. The parcellation derived here represents a characterization of the 5-HT system which is more stable and explains the underlying 5-HT molecular imaging data better than other atlases, and may hence be more sensitive to capture region-specific changes modulated by 5-HT. •We evaluate a previous parcellation of the 5-HT system and propose an improvement.•Our new parcellation achieves a higher explained variance and is more stable.•The parcellation includes less regions, so region-based analysis are less penalized.•mRNA data for the HTR1A, HTR1F and HTR2C genes are well captured by the parcellation.

Serotonin (5-HT) is a neurotransmitter critically involved in a broad range of brain functions and implicated in the pathophysiology of neuropsychiatric illnesses including major depression, anxiety and sleep disorders. Despite being widely distributed throughout the brain, there is limited knowledge on the contribution of 5-HT to intrinsic brain activity. The dorsal raphe (DR) and median raphe (MR) nuclei are the source of most serotonergic neurons projecting throughout the brain and thus provide a compelling target for a seed-based probe of resting-state activity related to 5-HT. Here we implemented a novel multimodal neuroimaging approach for investigating resting-state functional connectivity (FC) between DR and MR and cortical, subcortical and cerebellar target areas. Using [11C]DASB positron emission tomography (PET) images of the brain serotonin transporter (5-HTT) combined with structural MRI from 49 healthy volunteers, we delineated DR and MR and performed a seed-based resting-state FC analysis. The DR and MR seeds produced largely similar FC maps: significant positive FC with brain regions involved in cognitive and emotion processing including anterior cingulate, amygdala, insula, hippocampus, thalamus, basal ganglia and cerebellum. Significant negative FC was observed within pre- and postcentral gyri for the DR but not for the MR seed. We observed a significant association between DR and MR FC and regional 5-HTT binding. Our results provide evidence for a resting-state network related to DR and MR and comprising regions receiving serotonergic innervation and centrally involved in 5-HT related behaviors including emotion, cognition and reward processing. These findings provide a novel advance in estimating resting-state FC related to 5-HT signaling, which can benefit our understanding of its role in behavior and neuropsychiatric illnesses. •We investigated serotonin-related resting-state functional connectivity (FC).•We present a novel multi-modal method for delineating the dorsal and median raphe.•Functional connectivity of these nuclei at rest was evaluated.•Brain regions functionally connected to the raphe nuclei were identified.•Raphe FC was positively associated with serotonin transporter binding.

Background The development of filamentous fungal cells from spore to mature mycelium is influenced by a myriad of environmental conditions. Traditional methods for quantifying various fungal cell morphologies in liquid media via microscopy are labor-intensive and prone to user-dependent variability. Methods This study addresses these challenges by introducing a streamlined workflow for fungal cell analysis, utilizing ilastik-based image segmentation and Python for fast data preparation and analysis. The goal is to offer a user-friendly method for counting and analyzing different fungal cell morphologies and their sizes that is free of charge and accessible to users with minimal programming experience. Results The presented workflow significantly reduces the time required to determine cell developmental stages from microscopy images. It enables rapid results, as compared to manual counting. It also minimizes inter-user variability, enhancing consistency across analyses. It provides a high-throughput, automated solution for fungal cell analysis allowing users to process hundreds of images (n = 48-438) for analysis in a short time period (9-56 min). Conclusion This approach shows great potential for accelerating research and improving data reliability in biological studies. Furthermore, it provides laboratories in resource limited settings with a free to use solution.

Recent neuroimaging studies have revealed important aspects of the underlying pathophysiological mechanisms of migraine suggesting abnormal brain energy metabolism and altered functional connectivity. Proton magnetic resonance spectroscopy (1H-MRS) studies investigated migraine patients in the interictal or ictal state. This first-of-its-kind study aimed to investigate the whole migraine cycle using 1H-MRS and resting-state functional magnetic resonance imaging (fMRI). A migraine patient underwent 1H-MRS and resting-state fMRI for 21 consecutive days, regardless of whether he was in an interictal or ictal state. Metabolite ratios were assessed and compared to the intrinsic connectivity of subcortical brain areas. Probable migraine phase-dependent changes in N-acetyl aspartate (NAA)/total creatine (tCr) and choline (Cho)/tCr levels are found in the left occipital lobe and left basal ganglia. NAA reflects neuronal integrity and Cho cellular membrane turnover. Such abnormalities may increase the susceptibility to excitatory migraine triggers. Functional connectivity between the right hippocampus and right or left pallidum was strongly correlated to the NAA/Cho ratio in the right thalamus, suggesting neurochemical modulation of these brain areas through thalamic connections. To draw statistically significant conclusions a larger cohort is needed.

Seasonal Affective Disorder (SAD) is a subtype of Major Depressive Disorder characterized by seasonally occurring depression that often presents with atypical vegetative symptoms such as hypersomnia and carbohydrate craving. It has recently been shown that unlike healthy people, patients with SAD fail to globally downregulate their cerebral serotonin transporter (5-HTT) in winter, and that this effect seemed to be particularly pronounced in female S-carriers of the 5-HTTLPR genotype. The purpose of this study was to identify a 5-HTT brain network that accounts for the adaption to the environmental stressor of winter in females with the short 5-HTTLPR genotype, a specific subgroup previously reported to be at increased risk for developing SAD. Nineteen females, either S' carriers (L - and S-carriers) without SAD ( = 13, mean age 23.6 ± 3.2 year, range 19-28) or S' carriers with SAD ( = 6, mean age 23.7 ± 2.4, range 21-26) were PET-scanned with [ C]DASB during both summer and winter seasons (asymptomatic and symptomatic phase, 38 scans in total) in randomized order, defined as a 12-week interval centered on summer or winter solstice. We used a multivariate Partial Least Squares (PLS) approach with NPAIRS split-half cross-validation, to identify and map a whole-brain pattern of 5-HTT levels that distinguished the brains of females without SAD from females suffering from SAD. We identified a pattern of 5-HTT levels, distinguishing females with SAD from those without SAD; it included the right superior frontal gyrus, brainstem, globus pallidus (bilaterally) and the left hippocampus. Across seasons, female S' carriers without SAD showed nominally higher 5-HTT levels in these regions compared to female S' carriers with SAD, but the group difference was only significant in the winter. Female S' carriers with SAD, in turn, displayed robustly increased 5-HTT levels in the ventral striatum (bilaterally), right orbitofrontal cortex, middle frontal gyrus (bilaterally), extending to the left supramarginal gyrus, left precentral gyrus and left postcentral gyrus during winter compared to female S' carriers without SAD. The study is preliminary and limited by small sample size in the SAD group ( = 6). These findings provide novel exploratory evidence for a wintertime state-dependent difference in 5-HTT levels that may leave SAD females with the short 5-HTTLPR genotype more vulnerable to persistent stressors like winter. The affected brain regions comprise a distributed set of areas responsive to emotion, voluntary, and planned movement, executive function, and memory. The preliminary findings provide additional insight into the neurobiological components through which the anatomical distribution of serotonergic discrepancies between individuals genetically predisposed to SAD, but with different phenotypic presentations during the environmental stressor of winter, may constitute a potential biomarker for resilience against developing SAD.

The advent of susceptibility‐sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been introduced to segment iron‐rich brain regions, such as the substantia nigra, subthalamic nucleus, red nucleus, and dentate nucleus, these methods are largely unavailable and manual annotation remains the most used approach to label these regions. Furthermore, given their recent success in outperforming other segmentation approaches, convolutional neural networks (CNN) promise better performances. The aim of this study was thus to evaluate state‐of‐the‐art CNN architectures for the labeling of deep brain nuclei from SW images. We implemented five CNN architectures and considered ensembles of these models. Furthermore, a multi‐atlas segmentation model was included to provide a comparison not based on CNN. We evaluated two prediction strategies: individual prediction, where a model is trained independently for each region, and combined prediction, which simultaneously predicts multiple closely located regions. In the training dataset, all models performed with high accuracy with Dice coefficients ranging from 0.80 to 0.95. The regional SWI intensities and volumes from the models' labels were strongly correlated with those obtained from manual labels. Performances were reduced on the external dataset, but were higher or comparable to the intrarater reliability and most models achieved significantly better results compared to multi‐atlas segmentation. CNNs can accurately capture the individual variability of deep brain nuclei and represent a highly useful tool for their segmentation from SW images. In this study, we evaluated a range of convolutional neural network architectures in the task of segmenting deep brain nuclei from susceptibility weighted images. In the training dataset, all models performed with high accuracy with Dice coefficients ranging from 0.80 to 0.95. Performances were reduced on the external dataset, but were higher or comparable to the intrarater reliability and most models achieved significantly better results compared to multi‐atlas segmentation.

Selective serotonin reuptake inhibitors (SSRIs) are widely used for treating neuropsychiatric disorders. However, the exact mechanism of action and why effects can take several weeks to manifest is not clear. The hypothesis of neuroplasticity is supported by preclinical studies, but the evidence in humans is limited. Here, we investigate the effects of the SSRI escitalopram on presynaptic density as a proxy for synaptic plasticity. In a double-blind placebo-controlled study (NCT04239339), 32 healthy participants with no history of psychiatric or cognitive disorders were randomized to receive daily oral dosing of either 20 mg escitalopram ( n  = 17) or a placebo ( n  = 15). After an intervention period of 3–5 weeks, participants underwent a [ 11 C]UCB-J PET scan (29 with full arterial input function) to quantify synaptic vesicle glycoprotein 2A (SV2A) density in the hippocampus and the neocortex. Whereas we find no statistically significant group difference in SV2A binding after an average of 29 (range: 24–38) days of intervention, our secondary analyses show a time-dependent effect of escitalopram on cerebral SV2A binding with positive associations between [ 11 C]UCB-J binding and duration of escitalopram intervention. Our findings suggest that brain synaptic plasticity evolves over 3–5 weeks in healthy humans following daily intake of escitalopram. This is the first in vivo evidence to support the hypothesis of neuroplasticity as a mechanism of action for SSRIs in humans and it offers a plausible biological explanation for the delayed treatment response commonly observed in patients treated with SSRIs. While replication is warranted, these results have important implications for the design of future clinical studies investigating the neurobiological effects of SSRIs.

Using [ F]altanserin, a serotonin 2A receptor (5-HT R) antagonist Positron Emission Tomography (PET) tracer, a positive association between cortical 5-HT R binding and the inward-directed facets of neuroticism has been demonstrated in healthy individuals. Psilocybin, a 5-HT R agonist, shows promise for the treatment of depression, reducing neuroticism and mood symptoms potentially via hypothalamic-pituitary-adrenal (HPA) modulation. 5-HT R and neuroticism are both modulated by HPA axis function. In this study, we examined whether the association between 5-HT R binding and the inward facets of neuroticism can be replicated in an independent healthy cohort using the new 5-HT R agonist tracer [ C]Cimbi-36, and if their association is moderated by cortisol awakening response (CAR), an index of HPA axis function. If so, this could advance mechanistic insights into interventions that target the 5-HT R and reduce neuroticism. Eighty healthy volunteers underwent [ C]Cimbi-36 PET scans and completed the NEO personality inventory (NEO-PI-R) for the assessment of neuroticism. Salivary samples were available for determination of CAR in 70 of the participants. Using linear latent variable models, we evaluated the association between 5-HT R binding and inward facets of neuroticism, namely depression, anxiety, self-consciousness and vulnerability to stress, and whether CAR moderated this association. The study confirms the positive association between 5-HT R binding and the inward facets of neuroticism ( = 0.01, 95% CI = [0.0005: 0.02], = 0.04), and this association is independent of CAR ( = 0.33). The findings prompt consideration of whether novel interventions such as psilocybin that actively targets 5-HT R and causes changes in personality could be particularly beneficial if implemented as a targeted approach based on neuroticism profiles.

This retrospective study aimed to analyse the correlation between somatostatin receptor subtypes (SSTR 1-5) and maximum standardized uptake value (SUVmax) in meningioma patients using Gallium-68 DOTA-D-Phe1-Tyr3-octreotide Positron Emission Tomography ([68Ga]Ga-DOTATOC PET). Secondly, we developed a radiomic model based on apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance images (DWI MRI) to reproduce SUVmax.OBJECTIVEThis retrospective study aimed to analyse the correlation between somatostatin receptor subtypes (SSTR 1-5) and maximum standardized uptake value (SUVmax) in meningioma patients using Gallium-68 DOTA-D-Phe1-Tyr3-octreotide Positron Emission Tomography ([68Ga]Ga-DOTATOC PET). Secondly, we developed a radiomic model based on apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance images (DWI MRI) to reproduce SUVmax.The study included 51 patients who underwent MRI and [68Ga]Ga-DOTATOC PET before meningioma surgery. SUVmax values were quantified from PET images and tumour areas were segmented on post-contrast T1-weighted MRI and mapped to ADC maps. A total of 1940 radiomic features were extracted from the tumour area on each ADC map. A random forest regression model was trained to predict SUVmax and the model's performance was evaluated using repeated nested cross-validation. The expression of SSTR subtypes was quantified in 18 surgical specimens and compared to SUVmax values.METHODThe study included 51 patients who underwent MRI and [68Ga]Ga-DOTATOC PET before meningioma surgery. SUVmax values were quantified from PET images and tumour areas were segmented on post-contrast T1-weighted MRI and mapped to ADC maps. A total of 1940 radiomic features were extracted from the tumour area on each ADC map. A random forest regression model was trained to predict SUVmax and the model's performance was evaluated using repeated nested cross-validation. The expression of SSTR subtypes was quantified in 18 surgical specimens and compared to SUVmax values.The random forest regression model successfully predicted SUVmax values with a significant correlation observed in all 100 repeats (p < 0.05). The mean Pearson's r was 0.42 ± 0.07 SD, and the root mean square error (RMSE) was 28.46 ± 0.16. SSTR subtypes 2A, 2B, and 5 showed significant correlations with SUVmax values (p < 0.001, R2 = 0.669; p = 0.001, R2 = 0.393; and p = 0.012, R2 = 0.235, respectively).RESULTSThe random forest regression model successfully predicted SUVmax values with a significant correlation observed in all 100 repeats (p < 0.05). The mean Pearson's r was 0.42 ± 0.07 SD, and the root mean square error (RMSE) was 28.46 ± 0.16. SSTR subtypes 2A, 2B, and 5 showed significant correlations with SUVmax values (p < 0.001, R2 = 0.669; p = 0.001, R2 = 0.393; and p = 0.012, R2 = 0.235, respectively).SSTR subtypes 2A, 2B, and 5 correlated significantly with SUVmax in meningioma patients. The developed radiomic model based on ADC maps effectively reproduces SUVmax using [68Ga]Ga-DOTATOC PET.CONCLUSIONSSTR subtypes 2A, 2B, and 5 correlated significantly with SUVmax in meningioma patients. The developed radiomic model based on ADC maps effectively reproduces SUVmax using [68Ga]Ga-DOTATOC PET.