Risks and benefits of psilocybin use in people with bipolar disorder: An international web-based survey on experiences of 'magic mushroom' consumption.

BACKGROUND: Psilocybin, the primary psychoactive component of psychedelic 'magic mushrooms', may have potential for treating depressive symptoms, and consequent applications for bipolar disorder (BD). Knowledge of the risks and benefits of psilocybin in BD is limited to case studies. AIM: To support the design of clinical trials, we surveyed experiences of psilocybin use in people with BD. METHODS: An international web-based survey was used to explore experiences of psilocybin use in people with a self-reported diagnosis of BD. Quantitative findings were summarised using descriptive statistics. Qualitative content analysis was used to investigate free-text responses, with a focus on positive experiences of psilocybin use. RESULTS: A total of 541 people completed the survey (46.4% female, mean 34.1 years old). One-third (32.2%; n = 174) of respondents described new/increasing symptoms after psilocybin trips, prominently manic symptoms, difficulties sleeping and anxiety. No differences in rates of adverse events overall were observed between individuals with BD I compared to BD II. Use of emergency medical services was rare (n = 18; 3.3%), and respondents (even those who experienced adverse effects) indicated that psilocybin use was more helpful than harmful. Quantitative findings elaborated on perceived benefits, as well as the potential for psilocybin trips to contain both positively and negatively received elements. CONCLUSIONS: The subjective benefits of psilocybin use for mental health symptoms reported by survey participants encourage further investigation of psilocybin-based treatments for BD. Clinical trials should incorporate careful monitoring of symptoms, as data suggest that BD symptoms may emerge or intensify following psilocybin use.

"A sense of the bigger picture:" A qualitative analysis of follow-up interviews with people with bipolar disorder who self-reported psilocybin use.

OBJECTIVES: People with bipolar disorder (BD) spend more time depressed than manic/hypomanic, and depression is associated with greater impairments in psychosocial functioning and quality of life than mania/hypomania. Emerging evidence suggests psilocybin, the psychoactive compound in "magic mushrooms," is a promising treatment for unipolar depression. Clinical trials of psilocybin therapy have excluded people with BD as a precaution against possible adverse effects (e.g., mania). Our study centered the experiences of adults living with BD who consumed psilocybin-containing mushrooms, and aimed to (1) understand its subjective impacts on BD symptoms, (2) deepen understanding of Phase I survey results, and (3) elucidate specific contextual factors associated with adverse reactions in naturalistic settings. METHODS: Following an international survey (Phase I), follow-up interviews were conducted with 15 respondents (Phase II) to further understand psilocybin use among adults with BD. As part of a larger mixed-methods explanatory sequential design study, reflexive thematic analysis was used to elaborate findings. RESULTS: Three major themes containing sub-themes were developed. (1) Mental Health Improvements: (1.1) decreased impact and severity of depression, (1.2) increased emotion processing, (1.3) development of new perspectives, and (1.4) greater relaxation and sleep. (2) Undesired Mental Health Impacts: (2.1) changes in sleep, (2.2) increased mania severity, (2.3) hospitalization, and (2.4) distressing sensory experiences. (3) Salient Contextual Factors for psilocybin use included: (3.1) poly-substance use and psilocybin dose, (3.2) solo versus social experiences, and (3.3) pre-psilocybin sleep deprivation. CONCLUSION: Our findings demonstrate both benefits and risks of psilocybin use in this population. Carefully designed clinical trials focused on safety and preliminary efficacy are warranted.

Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features.

An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics--such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient--are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence.

Double Line Image Rotation.

This paper proposes a fast algorithm for rotating images while preserving their quality. The new approach rotates images based on vertical or horizontal lines in the original image and their rotated equation in the target image. The proposed method is a one-pass method that determines a based-line equation in the target image and extracts all corresponding pixels on the base-line. Floating-point multiplications are performed to calculate the base-line in the target image, and other line coordinates are calculated using integer addition or subtraction and logical justifications from the base-line pixel coordinates in the target image. To avoid a heterogeneous distance between rotated pixels in the target image, each line rotates to two adjacent lines. The proposed method yields good performance in terms of speed and quality according to the results of an analysis of the computation speed and accuracy.

Contour-based corner detection and classification by using mean projection transform.

Image corner detection is a fundamental task in computer vision. Many applications require reliable detectors to accurately detect corner points, commonly achieved by using image contour information. The curvature definition is sensitive to local variation and edge aliasing, and available smoothing methods are not sufficient to address these problems properly. Hence, we propose Mean Projection Transform (MPT) as a corner classifier and parabolic fit approximation to form a robust detector. The first step is to extract corner candidates using MPT based on the integral properties of the local contours in both the horizontal and vertical directions. Then, an approximation of the parabolic fit is calculated to localize the candidate corner points. The proposed method presents fewer false-positive (FP) and false-negative (FN) points compared with recent standard corner detection techniques, especially in comparison with curvature scale space (CSS) methods. Moreover, a new evaluation metric, called accuracy of repeatability (AR), is introduced. AR combines repeatability and the localization error (Le) for finding the probability of correct detection in the target image. The output results exhibit better repeatability, localization, and AR for the detected points compared with the criteria in original and transformed images.