Pseudocyesis as a healing mechanism for psychological trauma

Pseudocyesis, a rare condition characterized by clinical signs and symptoms of pregnancy except for the actual existence of a fetus, is a somatic symptom disorder associated with a variety of biological, psychological and social factors. The present report aims to present the case of a 45-year old patient with pseudocyesis from a psychodynamic perspective. According to a psychodynamic perspective and based on patient’s history, pseudocyesis functioned as a mental healing mechanismfor the trauma of long-standing infertility, the trauma of eight unsuccessful and painful in vitro fertilization attempts and above all, the trauma of finally giving birth to a non-healthy child

Multiple sclerosis and mental health related quality of life: The role of defense mechanisms, defense styles and family environment

Background: Multiple sclerosis is a demyelinating chronic neurologic disease that can lead to disability and thus to deterioration of quality of life. Psychological parameters such as ego defense mechanisms, defense styles and family environment are important factors in the adaptation process, and as such they can play important roles in QoL. This study aims to assess the psychological factors as well as the clinical and demographic characteristics related to mental health quality of life (MHQoL). Methods: This was an observational, cross-sectional study conducted in a sample of 90 people with MS in the years 2018–2020. All participants completed the following questionnaires: MSQoL-54, DSQ-88, LSI, FES-R, SOC, BDI-II, STAI. Disability was assessed using EDSS. Results:In multiple linear regression, significant roles were played by depression (R2: 41.1%, p: 0.001) and, to a lesser extent, the event of a relapse (R2: 3.5%, p: 0.005), expressiveness (R2: 3.6%, p < 0.05) and image distortion style (R2: 4.5%, p: 0.032). After performing a hierarchical-stepwise analysis (excluding depression), the important factors were maladaptive defense style (R2: 23.7%, p: 0.002), the event of relapse (R2: 8.1%, p < 0.001), expressiveness (R2: 5.5%, p: 0.004) and self-sacrificing defense style (R2: 2.4%, p: 0.071). Conclusion: Psychological factors play important roles in MHQoL of people with multiple sclerosis. Thus, neurologists should integrate in their practice an assessment by mental health specialists. Moreover, targeted psychotherapeutic interventions could be planned i to improve QoL

DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography

We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusion weighted images (DWI), which does not assume a specific diffusion model. We use a recurrent neural network for mapping sequences of DWI values into probabilistic fiber orientation distributions. Based on these estimations, our model facilitates both deterministic and probabilistic streamline tractography. We quantitatively evaluate our method using the Tractometer tool, demonstrating competitive performance with state-of-the art classical and machine learning based tractography algorithms. We further present qualitative results of bundle-specific probabilistic tractography obtained using our method. The code is publicly available at: https://github.com/itaybenou/DeepTract.git

Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student’s t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer’s disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM)

Convolutional neural networks for direct inference of pharmacokinetic parameters: Application to stroke dynamic contrast-enhanced MRI

Background and Purpose: The T1-weighted dynamic contrast enhanced (DCE)-MRI is an imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters characterizing microvasculature of tissues. For the present study, we propose a new machine learning (ML) based approach to directly estimate the PK parameters from the acquired DCE-MRI image-time series that is both more robust and faster than conventional model fitting. Materials and Methods: We specifically utilize deep convolutional neural networks (CNNs) to learn the mapping between the image-time series and corresponding PK parameters. DCE-MRI datasets acquired from 15 patients with clinically evident mild ischaemic stroke were used in the experiments. Training and testing were carried out based on leave-one-patient-out cross- validation. The parameter estimates obtained by the proposed CNN model were compared against the two tracer kinetic models: (1) Patlak model, (2) Extended Tofts model, where the estimation of model parameters is done via voxelwise linear and nonlinear least squares fitting respectively. Results: The trained CNN model is able to yield PK parameters which can better discriminate different brain tissues, including stroke regions. The results also demonstrate that the model generalizes well to new cases even if a subject specific arterial input function (AIF) is not available for the new data. Conclusion: A ML-based model can be used for direct inference of the PK parameters from DCE image series. This method may allow fast and robust parameter inference in population DCE studies. Parameter inference on a 3D volume-time series takes only a few seconds on a GPU machine, which is significantly faster compared to conventional non-linear least squares fitting

Knowledge and attitudes of medical students about clinical aspects of congenital cytomegalovirus infection in newborns: A nationwide cross-sectional study in Greece

IntroductionCytomegalovirus (CMV) is the most frequent cause of congenital infection worldwide causing severe morbidity in newborns, infants, and children. Despite the clinical importance of congenital CMV (cCMV) infection, studies conducted so far indicate that there is limited awareness in the medical community in the field. The aim of this study was to assess Greek medical students’ knowledge on cCMV infection.MethodsWe performed a questionnaire-based nationwide cross-sectional study. A convenience sample of medical students from seven medical schools was enrolled.ResultsOf the 562 respondents, 54,8% considered themselves undereducated on cCMV infection. However, almost half of the participants could correctly recognize some basic principles of cCMV infection including ways of transmission, diagnosis and treatment, while there were aspects of cCMV infection with knowledge deficit. The year of study had a positive impact on the level of knowledge with students of higher years of study being of more sufficient education on the specific topic.ConclusionOverall, our study indicates a discrepancy between self-reported awareness and the level of knowledge among medical students in Greece. Further educational opportunities about cCMV should be offered, particularly in areas of the curriculum involving the care of women and children. Establishing medical students’ solid background on the disease burden and educating them about preventative strategies for at-risk populations, should be the main pillars of such efforts in order to promote confidence in managing these cases in their future professional careers