Impact of coding risk variant IFNGR2 on the B cell-intrinsic IFN-γ signaling pathway in multiple sclerosis.

B cells of people with multiple sclerosis (MS) are more responsive to IFN-γ, corresponding to their brain-homing potential. We studied how a coding single nucleotide polymorphism (SNP) in IFNGR2 (rs9808753) co-operates with Epstein-Barr virus (EBV) infection as MS risk factors to affect the IFN-γ signaling pathway in human B cells. In both cell lines and primary cells, EBV infection positively associated with IFN-γ receptor expression and STAT1 phosphorylation. The IFNGR2 risk SNP selectively promoted downstream signaling via STAT1, particularly in transitional B cells. Altogether, EBV and the IFNGR2 risk SNP independently amplify IFN-γ signaling, potentially driving B cells to enter the MS brain.

Integrating Learning-based Priors with Physics-based Models in Ultrasound Elasticity Reconstruction.

Ultrasound elastography images which enable quantitative visualization of tissue stiffness can be reconstructed by solving an inverse problem. Classical model-based methods are usually formulated in terms of constrained optimization problems. To stabilize the elasticity reconstructions, regularization techniques such as Tikhonov method are used with the cost of promoting smoothness and blurriness in the reconstructed images. Thus, incorporating a suitable regularizer is essential for reducing the elasticity reconstruction artifacts while finding the most suitable one is challenging. In this work, we present a new statistical representation of the physical imaging model which incorporates effective signal-dependent colored noise modeling. Moreover, we develop a learning-based integrated statistical framework which combines a physical model with learning-based priors. We use a dataset of simulated phantoms with various elasticity distributions and geometric patterns to train a denoising regularizer as the learning-based prior. We use fixed-point approaches and variants of gradient descent for solving the integrated optimization task following learning-based plug-and-play (PnP) prior and regularization by denoising (RED) paradigms. Finally, we evaluate the performance of the proposed approaches in terms of relative mean square error (RMSE) with nearly 20% improvement for both piece-wise smooth simulated phantoms and experimental phantoms compared to the classical model-based methods and 12% improvement for both spatially-varying breast-mimicking simulated phantoms and an experimental breast phantom, demonstrating the potential clinical relevance of our work. Moreover, the qualitative comparisons of reconstructed images demonstrate the robust performance of the proposed methods even for complex elasticity structures that might be encountered in clinical settings.

2-D Slicewise Waveform Inversion of Sound Speed and Acoustic Attenuation for Ring Array Ultrasound Tomography Based on a Block LU Solver.

Ultrasound tomography is an emerging imaging modality that uses the transmission of ultrasound through tissue to reconstruct images of its mechanical properties. Initially, ray-based methods were used to reconstruct these images, but their inability to account for diffraction often resulted in poor resolution. Waveform inversion overcame this limitation, providing high-resolution images of the tissue. Most clinical implementations, often directed at breast cancer imaging, currently rely on a frequency-domain waveform inversion to reduce computation time. For ring arrays, ray tomography was long considered a necessary step prior to waveform inversion in order to avoid cycle skipping. However, in this paper, we demonstrate that frequency-domain waveform inversion can reliably reconstruct high-resolution images of sound speed and attenuation without relying on ray tomography to provide an initial model. We provide a detailed description of our frequency-domain waveform inversion algorithm with open-source code and data that we make publicly available.

Brain elastography in aging relates to fluid/solid trendlines.

The relatively new tools of brain elastography have established a general trendline for healthy, aging adult humans, whereby the brain's viscoelastic properties 'soften' over many decades. Earlier studies of the aging brain have demonstrated a wide spectrum of changes in morphology and composition towards the later decades of lifespan. This leads to a major question of causal mechanisms: of the many changes documented in structure and composition of the aging brain, which ones drive the long term trendline for viscoelastic properties of grey matter and white matter? The issue is important for illuminating which factors brain elastography is sensitive to, defining its unique role for study of the brain and clinical diagnoses of neurological disease and injury. We address these issues by examining trendlines in aging from our elastography data, also utilizing data from an earlier landmark study of brain composition, and from a biophysics model that captures the multiscale biphasic (fluid/solid) structure of the brain. Taken together, these imply that long term changes in extracellular water in the glymphatic system of the brain along with a decline in the extracellular matrix have a profound effect on the measured viscoelastic properties. Specifically, the trendlines indicate that water tends to replace solid fraction as a function of age, then grey matter stiffness decreases inversely as water fraction squared, whereas white matter stiffness declines inversely as water fraction to the 2/3 power, a behavior consistent with the cylindrical shape of the axons. These unique behaviors point to elastography of the brain as an important macroscopic measure of underlying microscopic structural change, with direct implications for clinical studies of aging, disease, and injury.

Integrative Approaches to Managing Gut Health.

PURPOSE OF REVIEW: To summarize key integrative approaches to managing common gastrointestinal conditions. RECENT FINDINGS: Lifestyle interventions like diet, exercise, and stress reduction impact the gut microbiome and gastrointestinal symptoms. Evidence supports mind-body therapies, herbs, certain supplements, and other modalities as complimentary approaches, when appropriate, for common conditions like irritable bowel syndrome or gastroesophageal reflux disease. An integrative approach optimizes both conventional treatments and incorporates lifestyle modifications, complimentary modalities, and the doctor-patient relationship.

Twin study dissects CXCR3+ memory B cells as non-heritable feature in multiple sclerosis.

BACKGROUND: In multiple sclerosis (MS), B cells are considered main triggers of the disease, likely as the result of complex interaction between genetic and environmental risk factors. Studies on monozygotic twins discordant for MS offer a unique way to reduce this complexity and reveal discrepant subsets. METHODS: In this study, we analyzed B cell subsets in blood samples of monozygotic twins with and without MS using publicly available data. We verified functional characteristics by exploring the role of therapy and performed separate analyses in unrelated individuals. FINDINGS: The frequencies of CXCR3+ memory B cells were reduced in the blood of genetically identical twins with MS compared to their unaffected twin siblings. Natalizumab (anti-VLA-4 antibody) was the only treatment regimen under which these frequencies were reversed. The CNS-homing features of CXCR3+ memory B cells were supported by elevated CXCL10 levels in MS cerebrospinal fluid and their in vitro propensity to develop into antibody-secreting cells. CONCLUSIONS: Circulating CXCR3+ memory B cells are affected by non-heritable cues in people who develop MS. This underlines the requirement of environmental risk factors such as Epstein-Barr virus in triggering these B cells. We propose that after CXCL10-mediated entry into the CNS, CXCR3+ memory B cells mature into antibody-secreting cells to drive MS. FUNDING: This work was supported by Nationaal MS Fonds (OZ2021-016), Stichting MS Research (19-1057 MS, 20-490f MS, and 21-1142 MS), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program grant agreement no. 882424, and the Swiss National Science Foundation (733 310030_170320, 310030_188450, and CRSII5_183478).

Ocrelizumab associates with reduced cerebrospinal fluid B and CD20dim CD4+ T cells in primary progressive multiple sclerosis.

The anti-CD20 monoclonal antibody ocrelizumab reduces disability progression in primary progressive multiple sclerosis. CD20 is a prototypical B-cell marker; however, subpopulations of CD4+ and CD8+ T cells in peripheral blood and cerebrospinal fluid also express low levels of CD20 (CD20dim). Therefore, direct targeting and depletion of these CD20dim T-cell subpopulations may contribute to the therapeutic effect of ocrelizumab. The aim of this observational cohort study was to compare CD20+ B-cell and CD20dim T-cell distributions between peripheral blood and cerebrospinal fluid of ocrelizumab-treated or ocrelizumab-untreated people with primary progressive multiple sclerosis. Ocrelizumab treatment was associated with depletion of circulating B cells and CD20dim CD4+ and CD20dim CD8+ T cells (P < 0.0001, P = 0.0016 and P = 0.0008, respectively) but, in cerebrospinal fluid, only with lower proportions of B cells and CD20dim memory CD4+ T cells (P < 0.0001 and P = 0.0043, respectively). The proportional prevalence of cerebrospinal fluid CD20dim memory CD8+ T cells was not significantly reduced (P = 0.1333). Only in cerebrospinal fluid, the proportions of CD20dim cells within CD4+ and not CD8+ T cells positive for CCR5, CCR6 and CXCR3 were reduced in ocrelizumab-treated participants. The proportion of CD20dim CD4+ T cells and abundance of CD4+ relative to CD8+ T cells in cerebrospinal fluid correlated positively with age (R = 0.6799, P = 0.0150) and Age-Related Multiple Sclerosis Severity score (R = 0.8087, P = 0.0014), respectively. We conclude that, in contrast to cerebrospinal fluid CD20dim CD8+ T cells, B cells and CD20dim CD4+ T cells are reduced in cerebrospinal fluid of people with primary progressive multiple sclerosis with an ocrelizumab-associated depletion of circulating B cells and CD20dim T cells. Therefore, these cells are likely to contribute to the therapeutic effects of ocrelizumab in people with primary progressive multiple sclerosis.

Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention.

Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from 1 min to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-to-moment network fluctuations. Recently, researchers have "unfurled" traditional FC matrices in "edge cofluctuation time series" which measure timepoint-by-timepoint cofluctuations between regions. Here we apply event-based and parametric fMRI analyses to edge time series to capture moment-to-moment fluctuations in networks related to attention. In two independent fMRI datasets examining young adults of both sexes in which participants performed a sustained attention task, we identified a reliable set of edges that rapidly deflects in response to rare task events. Another set of edges varies with continuous fluctuations in attention and overlaps with a previously defined set of edges associated with individual differences in sustained attention. Demonstrating that edge-based analyses are not simply redundant with traditional regions-of-interest-based approaches, up to one-third of reliably deflected edges were not predicted from univariate activity patterns alone. These results reveal the large potential in combining traditional fMRI analyses with edge time series to identify rapid reconfigurations in networks across the brain.

Amphotericin B-loaded natural latex dressing for treating Candida albicans wound infections using Galleria mellonella model.

Amphotericin B (AmB) is the gold standard for antifungal drugs. However, AmB systemic administration is restricted because of its side effects. Here, we report AmB loaded in natural rubber latex (NRL), a sustained delivery system with low toxicity, which stimulates angiogenesis, cell adhesion and accelerates wound healing. Physicochemical characterizations showed that AmB did not bind chemically to the polymeric matrix. Electronic and topographical images showed small crystalline aggregates from AmB crystals on the polymer surface. About 56.6% of AmB was released by the NRL in 120 h. However, 33.6% of this antifungal was delivered in the first 24 h due to the presence of AmB on the polymer surface. The biomaterial's excellent hemo- and cytocompatibility with erythrocytes and human dermal fibroblasts (HDF) confirmed its safety for dermal wound application. Antifungal assay against Candida albicans showed that AmB-NRL presented a dose-dependent behavior with an inhibition halo of 30.0 ± 1.0 mm. Galleria mellonella was employed as an in vivo model for C. albicans infection. Survival rates of 60% were observed following the injection of AmB (0.5 mg.mL-1) in G. mellonella larvae infected by C. albicans. Likewise, AmB-NRL (0.5 mg.mL-1) presented survival rates of 40%, inferring antifungal activity against fungus. Thus, NRL adequately acts as an AmB-sustained release matrix, which is an exciting approach, since this antifungal is toxic at high concentrations. Our findings suggest that AmB-NRL is an efficient, safe, and reasonably priced ($0.15) dressing for the treatment of cutaneous fungal infections.

Probing Tissue Viscoelasticity With STL Ultrasound Shearwave Spectroscopy Using Cole-Cole Diagrams.

OBJECTIVE: Viscoelasticity is mapped by dispersion in shearwave elastography. Incomplete spectral information of shearwaves is therefore used to estimate mechanical stiffness. We propose capturing the "full-waveform-information" of the shear wave spectra to better resolve complex shear modulus µ* (ω). Approach is validated on phantom models, animal tissues, and feasibility demonstrated on human post-delivery placenta. METHODS: We captured robust estimates of µ* in ex-vivo livers subjected to water bath ablation, glutaraldehyde exposure and in the placenta. RESULTS: Complex modulus at 200 Hz is more reflective of tissue stiffness than cross-correlation estimate. Bias increased in phantoms with higher gelatin (G) (0.65: 6% G) and oil (O) (0.58: 6% G and 40% O) concentration, compared to elastic phantoms with low stiffness (0.33: 3% G). Actual tissues also reported higher bias in cross-correlation estimate (rabbit liver: 0.61, porcine liver: 2.20, and human placenta: 0.63). Stiffness is sensitive to ablation temperature, where the overall modulus changed from 3.02 KPa at 16 °C to 2.75 KPa at 56 °C in water bath. With exposure to Glutaraldehyde, the overall modulus increased from 2.37 to 9.03 KPa. Reconstruction errors in the loss modulus decreased by 68% with the power law compared to a Maxwell model in porcine livers with Cole-Cole inverse fitting. CONCLUSION: Omitting Shear wave attenuation leads to bias. Reconstruction of rheological response with a model is sensitive to its architecture and also the framework. SIGNIFICANCE: We use "full spectral information" in ultrasound shear wave elastography to better map µ*(ω) changes in viscoelastic tissues.

Differential Runx3, Eomes, and T-bet expression subdivides MS-associated CD4+ T cells with brain-homing capacity.

Multiple sclerosis (MS) is a common and devastating chronic inflammatory disease of the CNS. CD4+ T cells are assumed to be the first to cross the blood-central nervous system (CNS) barrier and trigger local inflammation. Here, we explored how pathogenicity-associated effector programs define CD4+ T cell subsets with brain-homing ability in MS. Runx3- and Eomes-, but not T-bet-expressing CD4+ memory cells were diminished in the blood of MS patients. This decline reversed following natalizumab treatment and was supported by a Runx3+ Eomes+ T-bet- enrichment in cerebrospinal fluid samples of treatment-naïve MS patients. This transcription factor profile was associated with high granzyme K (GZMK) and CCR5 levels and was most prominent in Th17.1 cells (CCR6+ CXCR3+ CCR4-/dim ). Previously published CD28- CD4 T cells were characterized by a Runx3+ Eomes- T-bet+ phenotype that coincided with intermediate CCR5 and a higher granzyme B (GZMB) and perforin expression, indicating the presence of two separate subsets. Under steady-state conditions, granzyme Khigh Th17.1 cells spontaneously passed the blood-brain barrier in vitro. This was only found for other subsets including CD28- cells when using inflamed barriers. Altogether, CD4+ T cells contain small fractions with separate pathogenic features, of which Th17.1 seems to breach the blood-brain barrier as a possible early event in MS.

Sound Speed Estimation for Distributed Aberration Correction in Laterally Varying Media.

Spatial variation in sound speed causes aberration in medical ultrasound imaging. Although our previous work has examined aberration correction in the presence of a spatially varying sound speed, practical implementations were limited to layered media due to the sound speed estimation process involved. Unfortunately, most models of layered media do not capture the lateral variations in sound speed that have the greatest aberrative effect on the image. Building upon a Fourier split-step migration technique from geophysics, this work introduces an iterative sound speed estimation and distributed aberration correction technique that can model and correct for aberrations resulting from laterally varying media. We first characterize our approach in simulations where the scattering in the media is known a-priori. Phantom and in-vivo experiments further demonstrate the capabilities of the iterative correction technique. As a result of the iterative correction scheme, point target resolution improves by up to a factor of 4 and lesion contrast improves by up to 10.0 dB in the phantom experiments presented.

Shear wave elastography can stratify rectal cancer response to short-course radiation therapy.

Rectal cancer is a deadly disease typically treated using neoadjuvant chemoradiotherapy followed by total mesorectal excision surgery. To reduce the occurrence of mesorectal excision surgery for patients whose tumors regress from the neoadjuvant therapy alone, conventional imaging, such as computed tomography (CT) or magnetic resonance imaging (MRI), is used to assess tumor response to neoadjuvant therapy before surgery. In this work, we hypothesize that shear wave elastography offers valuable insights into tumor response to short-course radiation therapy (SCRT)-information that could help distinguish radiation-responsive from radiation-non-responsive tumors and shed light on changes in the tumor microenvironment that may affect radiation response. To test this hypothesis, we performed elastographic imaging on murine rectal tumors (n = 32) on days 6, 10, 12, 16, 18, 20, 23, and 25 post-tumor cell injection. The study revealed that radiation-responsive and non-radiation-responsive tumors had different mechanical properties. Specifically, radiation-non-responsive tumors showed significantly higher shear wave speed SWS (p < 0.01) than radiation-responsive tumors 11 days after SCRT. Furthermore, there was a significant difference in shear wave attenuation (SWA) (p < 0.01) in radiation-non-responsive tumors 16 days after SCRT compared to SWA measured just one day after SCRT. These results demonstrate the potential of shear wave elastography to provide valuable insights into tumor response to SCRT and aid in exploring the underlying biology that drives tumors' responses to radiation.

Vector-Mediated Delivery of Human Major Histocompatibility Complex-I into Hepatocytes Enables Investigation of T Cell Receptor-Redirected Hepatitis B Virus-Specific T Cells in Mice, and in Macaque Cell Cultures.

Adoptive T cell therapy using natural T cell receptor (TCR) redirection is a promising approach to fight solid cancers and viral infections in liver and other organs. However, clinical efficacy of such TCR+-T cells has been limited so far. One reason is that syngeneic preclinical models to evaluate safety and efficacy of TCR+-T cells are missing. We, therefore, developed an efficient viral vector strategy mediating expression of human major histocompatibility complex (MHC)-I in hepatocytes, which allows evaluation of TCR-T cell therapies targeting diseased liver cells. We designed adeno-associated virus (AAV) and adenoviral vectors encoding either the human-mouse chimeric HLA-A*02-like molecule, or fully human HLA-A*02 and human ß2 microglobulin (hß2m). Upon transduction of murine hepatocytes, the HLA-A*02 construct proved superior in terms of expression levels, presentation of endogenously processed peptides and activation of murine TCR+-T cells grafted with HLA-A*02-restricted, hepatitis B virus (HBV)-specific TCRs. In vivo, these T cells elicited effector function, controlled HBV replication, and reduced HBV viral load and antigen expression in livers of those mice that had received AAV-HBV and AAV-HLA-A*02. We then demonstrated the broad utility of this approach by grafting macaque T cells with the HBV-specific TCRs and enabling them to recognize HBV-infected primary macaque hepatocytes expressing HLA-A*02 upon adenoviral transduction. In conclusion, AAV and adenovirus vectors are suitable for delivery of HLA-A*02 and hß2m into mouse and macaque hepatocytes. When recognizing their cognate antigen in HLA-A*02-transduced mouse livers or on isolated macaque hepatocytes, HLA-A*02-restricted, HBV-specific TCR+-T cells become activated and exert antiviral effector functions. This approach is applicable to any MHC restriction and target disease, paving the way for safety and efficacy studies of human TCR-based therapies in physiologically relevant preclinical animal models.

Edge-based general linear models capture high-frequency fluctuations in attention.

Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from one minute to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-by-moment network fluctuations. Recently, researchers have 'unfurled' traditional FC matrices in 'edge cofluctuation time series' which measure time point-by-time point cofluctuations between regions. Here we apply event-based and parametric fMRI analyses to edge time series to capture high-frequency fluctuations in networks related to attention. In two independent fMRI datasets in which participants performed a sustained attention task, we identified a reliable set of edges that rapidly deflects in response to rare task events. Another set of edges varies with continuous fluctuations in attention and overlaps with a previously defined set of edges associated with individual differences in sustained attention. Demonstrating that edge-based analyses are not simply redundant with traditional regions-of-interest based approaches, up to one-third of reliably deflected edges were not predicted from univariate activity patterns alone. These results reveal the large potential in combining traditional fMRI analyses with edge time series to identify rapid reconfigurations in networks across the brain.

Mapping estimates of vascular permeability with a clinical indocyanine green fluorescence imaging system in experimental pancreatic adenocarcinoma tumors.

Significance: Pancreatic cancer tumors are known to be avascular, but their neovascular capillaries are still chaotic leaky vessels. Capillary permeability could have significant value for therapy assessment, and its quantification might be possible with macroscopic imaging of indocyanine green (ICG) kinetics in tissue. Aim: The capacity of using standard fluorescence surgical systems for ICG kinetic imaging as a probe for capillary leakage was evaluated using a clinical surgical fluorescence imaging system, as interpreted through vascular permeability modeling. Approach: Xenograft pancreatic adenocarcinoma models were imaged in mice during bolus injection of ICG to capture the kinetics of uptake. Image analysis included ratiometric data, normalization, and match to theoretical modeling. Kinetic data were converted into the extraction fraction of the capillary leakage. Results: Pancreatic tumors were usually less fluorescent than the surrounding healthy tissues, but still the rate of tumor perfusion could be assessed to quantify capillary extraction. Model simulations showed that flow kinetics stabilized after about 1 min beyond the initial bolus injection and that the relative extraction fraction model estimates matched the experimental data of normalized uptake within the tissue. The kinetics in the time period of 1 to 2 min post-injection provided optimal differential data between AsPC1 and BxPC3 tumors, although high individual variation exists between tumors. Conclusions: ICG kinetic imaging during the initial leakage phase was diagnostic for quantitative vascular permeability within pancreatic tumors. Methods for autogain correction and normalized model-based interpretation allowed for quantification of extraction fraction and difference identification between tumor types in early timepoints.

Epstein-Barr virus and genetic risk variants as determinants of T-bet+ B cell-driven autoimmune diseases.

B cells expressing the transcription factor T-bet are found to have a protective role in viral infections, but are also considered major players in the onset of different types of autoimmune diseases. Currently, the exact mechanisms driving such 'atypical' memory B cells to contribute to protective immunity or autoimmunity are unclear. In addition to general autoimmune-related factors including sex and age, the ways T-bet+ B cells instigate autoimmune diseases may be determined by the close interplay between genetic risk variants and Epstein-Barr virus (EBV). The impact of EBV on T-bet+ B cells likely relies on the type of risk variants associated with each autoimmune disease, which may affect their differentiation, migratory routes and effector function. In this hypothesis-driven review, we discuss the lines of evidence pointing to such genetic and/or EBV-mediated influence on T-bet+ B cells in a range of autoimmune diseases, including systemic lupus erythematosus (SLE) and multiple sclerosis (MS). We provide examples of how genetic risk variants can be linked to certain signaling pathways and are differentially affected by EBV to shape T-bet+ B-cells. Finally, we propose options to improve current treatment of B cell-related autoimmune diseases by more selective targeting of pathways that are critical for pathogenic T-bet+ B-cell formation.

Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview.

Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.

Mini-review: The neurobiology of treating substance use disorders with classical psychedelics.

The potential of psychedelics to persistently treat substance use disorders is known since the 1960s. However, the biological mechanisms responsible for their therapeutic effects have not yet been fully elucidated. While it is known that serotonergic hallucinogens induce changes in gene expression and neuroplasticity, particularly in prefrontal regions, theories on how specifically this counteracts the alterations that occur in neuronal circuitry throughout the course of addiction are largely unknown. This narrative mini-review endeavors to synthesize well-established knowledge from addiction research with findings and theories regarding the neurobiological effects of psychedelics to give an overview of the potential mechanisms that underlie the treatment of substance use disorders with classical hallucinogenic compounds and point out gaps in the current understanding.