TSPO ligands are promising alternatives to benzodiazepines in the treatment of anxiety, as they display less pronounced side effects such as sedation, cognitive impairment, tolerance development and abuse potential. In a randomized double-blind repeated-measures study we compare a benzodiazepine (alprazolam) to a TSPO ligand (etifoxine) by assessing side effects and acquiring resting-state fMRI data from 34 healthy participants after 5 days of taking alprazolam, etifoxine or a placebo. To study the effects of the pharmacological interventions in fMRI in detail and across different scales, we combine in our study complementary analysis strategies related to whole-brain functional network connectivity, local connectivity analysis expressed in regional homogeneity, fluctuations in low-frequency BOLD amplitudes and coherency of independent resting-state networks. Participants reported considerable adverse effects such as fatigue, sleepiness and concentration impairments, related to the administration of alprazolam compared to placebo. In resting-state fMRI we found a significant decrease in functional connection density, network efficiency and a decrease in the networks rich-club coefficient related to alprazolam. While observing a general decrease in regional homogeneity in high-level brain networks in the alprazolam condition, we simultaneously could detect an increase in regional homogeneity and resting-state network coherence in low-level sensory regions. Further we found a general increase in the low-frequency compartment of the BOLD signal. In the etifoxine condition, participants did not report any significant side effects compared to the placebo, and we did not observe any corresponding modulations in our fMRI metrics. Our results are consistent with the idea that sedation globally disconnects low-level functional networks, but simultaneously increases their within-connectivity. Further, our results point towards the potential of TSPO ligands in the treatment of anxiety and depression.

Recently, the gamma-aminobutyric acid (GABA) system has come into focus for the treatment of anxiety, postpartum depression, and major depressive disorder. Endogenous 3α-reduced steroids such as allopregnanolone are potent positive allosteric modulators of GABAA receptors and have been known for decades. Current industry developments and first approvals by the U.S. food and drug administration (FDA) for the treatment of postpartum depression with exogenous analogues of these steroids represent a major step forward in the field. 3α-reduced steroids target both synaptic and extrasynaptic GABAA receptors, unlike benzodiazepines, which bind to synaptic receptors. The first FDA-approved 3α-reduced steroid for postpartum depression is brexanolone, an intravenous formulation of allopregnanolone. It has been shown to provide rapid relief of depressive symptoms. An orally available 3α-reduced steroid is zuranolone, which also received FDA approval in 2023 for the treatment of postpartum depression. Although a number of studies have been conducted, the efficacy data were not sufficient to achieve approval of zuranolone in major depressive disorder by the FDA in 2023. The most prominent side effects of these 3α-reduced steroids are somnolence, dizziness and headache. In addition to the issue of efficacy, it should be noted that current data limit the use of these compounds to two weeks. An alternative to exogenous 3α-reduced steroids may be the use of substances that induce endogenous neurosteroidogenesis, such as the translocator protein 18 kDa (TSPO) ligand etifoxine. TSPO has been extensively studied for its role in steroidogenesis, in addition to other functions such as anti-inflammatory and neuroregenerative properties. Currently, etifoxine is the only clinically available TSPO ligand in France for the treatment of anxiety disorders. Studies are underway to evaluate its antidepressant potential. Hopefully, neurosteroid research will lead to the development of fast-acting antidepressants.

Abstract Background Recent developments suggest that neurosteroids may achieve rapid antidepressant effects. As such, neurosteroidogenesis mediated by the translocator protein 18 kDa (TSPO) might constitute a promising option for the treatment of depression. Therefore, the current clinical trial aims to get the first evidence of whether TPSO ligands promote rapid antidepressant effects. Furthermore, we study which mechanisms of action, e.g., modulation of distinct neuronal networks, neurosteroidogenesis, endocrinological mechanisms, TSPO expression or microbiome composition, contribute to their putative antidepressant effects. Methods This is a randomized, placebo-controlled, double-blind single-center trial of 2-week treatment with the TSPO ligand etifoxine versus placebo in depressive patients. Main eligibility criteria: male or female individuals aged 18 to 65 years with unipolar/bipolar depressive disorder with no other psychiatric main diagnosis or acute neurological/somatic disorder or drug/alcohol dependence during their lifetime. The primary endpoint is the time point at which 50% of the maximal effect has occurred (ET50) estimated by the scores of the Hamilton Depression Scale (HAMD-21). A total of 20 patients per group are needed to detect changes of therapeutic efficacy about 5% and changes of ET50 about 10% with a power of 70%. Assuming a drop-out rate of 10–20%, 50 patients will be randomized in total. The study will be conducted at the Department of Psychiatry and Psychotherapy of the University of Regensburg. Discussion This study will provide a first proof-of-concept on the potential of the TSPO ligand etifoxine in the treatment of depressive disorders. Trial registration Clinical Trials Register (EudraCT number: 2021-006773-38, registration date: 14 September 2022) and German Register of Clinical Studies (DRKS number: DRKS00031099, registration date: 23 January 2023).

The functional organization of brain networks maintains a delicate equilibrium between segregation and integration where it facilitates local neural communication together with effective global integration of information across network’s components. While numerous whole-brain imaging studies have linked alterations in functional topology to major depressive disorder (MDD), our comprehension of how these changes manifest at the cellular level remains limited. Here, we explored whether neuronal networks derived from induced pluripotent stem cells (hiPSCs) of nine depressed patients display a distinct functional topology compared to those of matched controls. Spontaneous activity of the derived neuronal networks was captured using calcium imaging, and graph theory analysis was applied to assess functional topology. We computed the graph metrics clustering coefficient and global efficiency to quantify respective network segregation and integration attributes. We also measured the average node degree to assess group differences in the overall number of connections. We observed a decrease in clustering coefficient and average node degree in MDD-derived neural networks compared to those of controls. Global efficiency also exhibited a decreasing trend in patient-derived networks across varying thresholds and network sizes. Together, our findings reveal diminished segregation properties and a reduced number of nodal connections in MDD-derived neural networks, suggesting a predisposition for a less efficient functional topology in depression already at the microscale. This work marks the first attempt to explore microscale alterations in functional topology of human-derived neural networks in MDD and highlights the power of iPSC technology in providing a human cellular model to better understand disease mechanisms.

IntroductionIn contrast to other sensory domains, detection of primary olfactory processes using functional magnetic resonance imaging has proven to be notably challenging with conventional block designs. This difficulty arises from significant habituation and hemodynamic responses in olfactory areas that do not appear to align with extended boxcar functions convolved with a generic hemodynamic response model. Consequently, some researchers have advocated for a transition to event-related designs, despite their known lower detection power compared to block designs.MethodsHere, we conducted a block design experiment with 16s of continuous odorant stimulation alternating with 16s of continuous odorless air stimulation in 33 healthy participants. We compared four statistical analyses that relied either on standard block designs (SBD1-2) or on block designs that were modulated by the participants' individual breathing patterns (MBD1-2).ResultsWe found that such modulated block designs were comparatively more powerful than standard block designs, despite having a substantially lower design efficiency. Using whole-brain effect size maps, we observed that the right insular and medial aspects of the left piriform cortex exhibited a preference for a breathing-modulated analysis approach.DiscussionResearch in olfaction that necessitates designs with longer-lasting blocks, such as those employed in the investigation of state-dependent processing, will benefit from the breathing-modulated analyses outlined in this study.

In this paper, we present a new approach to determine the estimated time of arrival (ETA) for bus routes using (Deep) Graph Convolutional Networks (DGCNs). In addition, we use the same DGCN to detect detours within a route. In our application, a classification of routes and their underlying graph structure is performed using Graph Learning. Our model leads to a fast prediction and avoids solving the vehicle routing problem (VRP) through expensive computations. Moreover, we describe how to predict travel time for all routes using the same DGCN Model. This method makes it possible not to use a more computationally intensive approximation algorithm when determining long travel times with many intermediate stops, but to use our network for an early estimate of the quality of a route. Long travel times, in our case result from the use of a call-bus system, which must distribute many passengers among several vehicles and can take them to places without a regular stop. For a case study, the rural town of Roding in Bavaria is used. Our training data for this area results from an approximation algorithm that we implemented to optimize routes and generate an archive of routes of varying quality simultaneously.

The perception of coherent form configurations in natural scenes relies on the activity of early visual areas that respond to local orientation cues. Subsequently, high-level visual areas pool these local signals to construct a global representation of the initial visual input. However, it is still debated whether neurons in the early visual cortex respond also to global form features. Glass patterns (GPs) are visual stimuli employed to investigate local and global form processing and consist of randomly distributed dots pairs called dipoles arranged to form specific global configurations. In the current study, we used GPs and functional magnetic resonance imaging (fMRI) adaptation to reveal the visual areas that subserve the processing of oriented GPs. Specifically, we adapted participants to vertically oriented GP, then we presented test GPs having either the same or different orientations with respect to the adapting GP. We hypothesized that if local form features are processed exclusively by early visual areas and global form by higher-order visual areas, then the effect of visual adaptation should be more pronounced in higher tier visual areas as it requires global processing of the pattern. Contrary to this expectation, our results revealed that adaptation to GPs is robust in early visual areas (V1, V2, and V3), but not in higher tier visual areas (V3AB and V4v), suggesting that form cues in oriented GPs are primarily derived from local-processing mechanisms that originate in V1. Finally, adaptation to vertically oriented GPs causes a modification in the BOLD response within early visual areas, regardless of the relative orientations of the adapting and test stimuli, indicating a lack of orientation selectivity.

In this study, an automated 2D machine learning approach for fast and precise segmentation of MS lesions from multi-modal magnetic resonance images (mmMRI) is presented. The method is based on an U-Net like convolutional neural network (CNN) for automated 2D slice-based-segmentation of brain MRI volumes. The individual modalities are encoded in separate downsampling branches without weight sharing, to leverage the specific features. Skip connections input feature maps to multi-scale feature fusion (MSFF) blocks at every decoder stage of the network. Those are followed by multi-scale feature upsampling (MSFU) blocks which use the information about lesion shape and location. The CNN is evaluated on two publicly available datasets: The ISBI 2015 longitudinal MS lesion segmentation challenge dataset containing 19 subjects and the MICCAI 2016 MSSEG challenge dataset containing 15 subjects from various scanners. The proposed multi-input 2D architecture is among the top performing approaches in the ISBI challenge, to which open-access papers are available, is able to outperform state-of-the-art 3D approaches without additional post-processing, can be adapted to other scanners quickly, is robust against scanner variability and can be deployed for inference even on a standard laptop without a dedicated GPU.

In this study, an automated machine learning approach for the segmentation of MS lesions from multi- modal magnetic resonance images (mmMRI) is presented. The method is based on a U-Net like convolutional neural network (CNN) for 2D slice-based segmentation of 3D brain MRI volumes. The different modalities are encoded in sep- arate downsampling channels. The skip connections input feature maps to multi-scale feature fusion blocks at every stage of the network. These are followed by multi-scale feature upsampling blocks, which use the information from lesion shape and location. The CNN is evaluated on two publicly available datasets: The ISBI 2015 longitudinal MS lesion segmentation challenge dataset which contains 14 MS patients and the MICCAI 2016 MSSEG challenge dataset consisting of 15 MS patients. Regarding the ISBI Challenge, the proposed method was among the top performing ap- proaches to which open-access papers are available. The MICCAI dataset served to evaluate the robustness of the architecture against scanner variability and to show the improvements in performance after transfer learning.

Zielsetzung Entwicklung eines vollautomatischen, zuverlässigen und schnellen Werkzeugs zur Segmentierung von Multiple-Sklerose Läsionen für multimodale MRT-Scans mit Hilfe von Deep Learning.

There is need for novel fast acting treatment options in affective disorders. 3α-reduced neurosteroids such as allopregnanolone are powerful positive allosteric modulators of GABA A receptors and target also extrasynaptic receptors. Their synthesis is mediated by the translocator protein 18 kDa (TSPO). TSPO ligands not only promote endogenous neurosteroidogenesis, but also exert a broad spectrum of functions involving modulation of mitochondrial activity and acting as anti-inflammatory and neuroregenerative agents. Besides affective symptoms, in depression cognitive impairment can be frequently observed, which may be ameliorated through targeting of extrasynaptic GABA A receptors either via TSPO ligands or exogenously administered 3α-reduced neurosteroids. Interestingly, recent findings indicate an enhanced activation of the complement system, e.g., enhanced expression of C1q, both in depression and dementia. It is of note that benzodiazepines have been shown to reduce long-term potentiation and to cause cognitive decline. Intriguingly, TSPO may be crucial in mediating the effects of benzodiazepines on synaptic pruning. Here, we discuss how benzodiazepines and TSPO may interfere with synaptic pruning. Moreover, we highlight recent developments of TSPO ligands and 3α-reduced neurosteroids as therapeutic agents. Etifoxine is the only clinically available TSPO ligand so far and has been studied in anxiety disorders. Regarding 3α-reduced neurosteroids, brexanolone, an intravenous formulation of allopregnanolone, has been approved for the treatment of postpartum depression and zuranolone, an orally available 3α-reduced neurosteroid, is currently being studied in major depressive disorder and postpartum depression. As such, 3α-reduced neurosteroids and TSPO ligands may constitute promising treatment approaches for affective disorders.

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph-structured signals like those observed in complex brain networks. In our study we compare different spatiotemporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN-based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multimodal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatiotemporal dynamics in brain networks.

This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.

A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components.

Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.