Automated deep learning segmentation of high-resolution 7 Tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases.

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high-resolution dataset of 135 postmortem human brain tissue specimens imaged at 0.3 mm3 isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We evaluate the reliability of this pipeline via overlap metrics with manual segmentation in 6 specimens, and intra-class correlation between cortical thickness measures extracted from the automatic segmentation and expert-generated reference measures in 36 specimens. We also segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter, providing a limited evaluation of accuracy. We show generalizing capabilities across whole-brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm3 and 0.16 mm3 isotropic T2*w fast low angle shot (FLASH) sequence at 7T. We report associations between localized cortical thickness and volumetric measurements across key regions, and semi-quantitative neuropathological ratings in a subset of 82 individuals with Alzheimer's disease (AD) continuum diagnoses. Our code, Jupyter notebooks, and the containerized executables are publicly available at the project webpage (https://pulkit-khandelwal.github.io/exvivo-brain-upenn/).

Perceived challenges in treatment decision-making for endometriosis: healthcare professional perspectives.

Background: Endometriosis, a systemic chronic inflammatory condition which has no cure, has a high symptom burden that can negatively impact every facet of life. Given the absence of a gold-standard treatment, the best symptom management regimen in endometriosis is heavily reliant on a patient's values and preferences, making shared decision-making (SDM) vital. However, a comprehensive patient decision aid (PtDA) intervention that could facilitate patient decision-making and promote SDM is lacking in endometriosis, and there is little research on the decisional support needs of individuals with this condition. This qualitative study aimed to explore healthcare professional (HP) perspectives of their clients' decisional support needs when choosing treatments to manage endometriosis symptoms, with a view to evaluating the need for a PtDA. Methods: Australian HPs identified as specialising in endometriosis care (N = 13) were invited to participate in a short interview over the Internet by phone. Questions focussed on perceived facilitators and challenges of decision-making when choosing treatments for endometriosis. Transcribed qualitative data were thematically analysed and verified by multiple coders, using the template approach. Results: Four themes were identified: (1) Identifying and setting priorities; (2) HPs' lack of time and perceived lack of knowledge; (3) Patient-centred care and SDM, including patient capacity; and (4) Decision-making blinded by hope. This is the first known study to explore HPs' perspectives on patient decision-making challenges in endometriosis. Discussion: Findings draw attention to the difficulties people with endometriosis experience when assessing and choosing treatments, highlighting the need for a comprehensive PtDA intervention to support this decision-making.

The effectiveness of psychological interventions for reducing poor body image in endometriosis, PCOS and other gynaecological conditions: a systematic review and meta-analysis.

Gynaecological conditions (e.g., endometriosis, PCOS) result in bodily changes that negatively impact body image. Psychological interventions (e.g., CBT, psychoeducation) have shown promise in reviews with the general population for alleviating body image concerns. This systematic review and meta-analysis aims to provide asynthesis of the impact of psychological interventions for reducing body image concerns for individuals with gynaecological conditions. Electronic databases were searched for relevant psychological intervention studies with body image outcomes. Twenty-one eligible studies were included in the systematic review (ten were included in a random-effects meta-analysis). Studies included participants (N = 1483, M = 71.85, SD = 52.79) with a range of gynaecological conditions, ages (Mage = 35.08, SD = 12.17) and cultural backgrounds. Most included studies reported at least one positive effect with the meta-analysis indicating psychological interventions were moderately superior to control conditions for reducing body image concerns (SMD -.41, 95% CI [-0.20 -0.62]). However, there was a high risk of bias and moderate heterogeneity. Results suggest psychological interventions may hold promise for reducing body image concerns among individuals gynaecological conditions in the short term. Further, preliminary support was found for the use of theory-guided psychological interventions delivered in group settings in particular, with further research needed on optimal intervention length and particular psychotherapeutic approach.