Stretched sinograms for limited-angle tomographic reconstruction with neural networks

We present a direct method for limited angle tomographic reconstruction using convolutional networks. The key to our method is to first stretch every tilt view in the direction perpendicular to the tilt axis by the secant of the tilt angle. These stretched views are then fed into a 2-D U-Net which directly outputs the 3-D reconstruction. We train our networks by minimizing the mean squared error between the network's generated reconstruction and a ground truth 3-D volume. To demonstrate and evaluate our method, we synthesize tilt views from a 3-D image of fly brain tissue acquired with Focused Ion Beam Scanning Electron Microscopy. We compare our method to using a U-Net to directly reconstruct the unstretched tilt views and show that this simple stretching procedure leads to significantly better reconstructions. We also compare to using a network to clean up reconstructions generated by backprojection and filtered backprojection, and find that this simple stretching procedure also gives lower mean squared error on previously unseen images

DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization

Inverse problems generally require a regularizer or prior for a good solution. A recent trend is to train a convolutional net to denoise images, and use this net as a prior when solving the inverse problem. Several proposals depend on a singular value decomposition of the forward operator, and several others backpropagate through the denoising net at runtime. Here we propose a simpler approach that combines the traditional gradient-based minimization of reconstruction error with denoising. Noise is also added at each step, so the iterative dynamics resembles a Langevin or diffusion process. Both the level of added noise and the size of the denoising step decay exponentially with time. We apply our method to the problem of tomographic reconstruction from electron micrographs acquired at multiple tilt angles. With empirical studies using simulated tilt views, we find parameter settings for our method that produce good results. We show that high accuracy can be achieved with as few as 50 denoising steps. We also compare with DDRM and DPS, more complex diffusion methods of the kinds mentioned above. These methods are less accurate (as measured by MSE and SSIM) for our tomography problem, even after the generation hyperparameters are optimized. Finally we extend our method to reconstruction of arbitrary-sized images and show results on 128 $\times$ 1568 pixel imagesComment: Solving inverse problems using gradient minimization coupled with a diffusion prio

Is Cognitive Performance Affecting Your Social Life? Cognitive Performance and its Relation to Social Functioning in Psychometric Schizotypy

poster abstractInterpreting cues and appropriately performing in social situations are two skills that are crucial for an individual to function in a modern society. Assessing social functioning and social cognition are used to measure these abilities in first-episode and schizophrenia research. The current study addresses the relationship between social cognition, neurocognition, and social functioning in participants with psychometric schizotypy; a cluster of traits thought to denote increased risk of developing psychosis. Undergraduate students pre-screened for schizotypy were tested for social cognitive and neurocognitive deficits, and lower social functioning. Significant positive correlations were observed between sub-tests of neurocognition and the social cognition measures. The current study shows that there are some subareas of neurocognition that are more closely related to social cognition than others. Independent T-tests reveal that individuals with psychometric schizotypy exhibit lower social functioning. Also, within the schizotypy group, participants report lower social functioning, including in their ability to create and maintain romantic relationships. Future research on this topic could try to find further explanations for social functioning deficits, as they do not appear to be explained by problems with social cognition

Stigma resistance at the personal, peer, and public levels: A new conceptual model.

Stigma resistance is consistently linked with key recovery outcomes, yet theoretical work is limited. This study explored stigma resistance from the perspective of individuals with serious mental illness (SMI). Twenty-four individuals with SMI who were either peer-service providers (those with lived experience providing services; N = 14) or consumers of mental health services (N = 10) engaged in semistructured interviews regarding experiences with stigma, self-stigma, and stigma resistance, including key elements of this process and examples of situations in which they resisted stigma. Stigma resistance is an ongoing, active process that involves using one’s experiences, knowledge, and sets of skills at the (1) personal, (2) peer, and (3) public levels. Stigma resistance at the personal level involves (a) not believing stigma or catching and challenging stigmatizing thoughts, (b) empowering oneself by learning about mental health and recovery, (c) maintaining one’s recovery and proving stigma wrong, and (d) developing a meaningful identity apart from mental illness. Stigma resistance at the peer level involves using one’s experiences to help others fight stigma and at the public level, resistance involved (a) education, (b) challenging stigma, (c) disclosing one’s lived experience, and (d) advocacy work. Findings present a more nuanced conceptualization of resisting stigma, grounded in the experiences of people with SMI. Stigma resistance is an ongoing, active process of using one’s experiences, skills, and knowledge to develop a positive identity. Interventions should consider focusing on personal stigma resistance early on and increasing the incorporation of peers into services

Examining Affect in Psychometric Schizotypy Using Behavioral Experience Sampling Methodology

poster abstractIn schizophrenia, patients often experience more negative emotions in the form of anger, sadness, and anxiety when compared to the general population. One unique way of measuring affect outside of the laboratory has been to use Experience Sampling Methods (ESM) to assess how individuals perceive current emotions in their daily life. However, these methods are still subject to self-report bias. In this study, we examined affect using traditional ESM methods while also implementing the Electronically Activated Recorder (EAR), a behaviorally-based ESM measure that provides real-world assessments of speech. To examine the EAR, we evaluated affect in schizotypy and non-schizotypy groups. Research shows that schizophrenia-like experiences, like increased negative affect, run along a continuum. Schizotypy is a category on the healthier end of the schizophrenia-spectrum; it applies to individuals who are thought to have a putative genetic liability for schizophrenia. Using the Linguistic Inquiry and Word Count (LIWC), we compared affective word usage among schizotypy and non-schizotypy groups to provide a real-world, behavioral ESM measure. When traditional ESM measures were used, we found individuals with schizotypy reported less negative emotions compared to the non-schizotypy group, but results did not reach the level of significance. We also observed that non-schizotypy individuals reported slightly higher positive emotions, and the schizotypy group reported slightly higher negative emotions. A similar pattern was observed when examining EAR data. Overall, results suggested that traditional and behavioral ESM measures of affect had significant overlap. In general, those with schizotypy demonstrated slightly more negative emotion and slightly less positive emotion than the non-schizotypy group. Findings did not reach the level of significance. This study demonstrates that the EAR provides behavioral ratings of affect that are on par with traditional ESM ratings. Future work should examine the EAR at different points on the schizophrenia-spectrum

Category fluency in psychometric schizotypy: How altering emotional valence and cognitive load affects performance

Introduction. In clinical high-risk populations, category fluency deficits are associated with conversion to psychosis. However, their utility as clinical risk markers is unclear in psychometric schizotypy, a group experiencing schizophrenia-like traits that is at putative high risk for psychosis. Methods. We examined whether introducing affective or cognitive load, two important stress vulnerability markers, altered category fluency performance in schizotypy (n = 42) and non-schizotypy (n = 38) groups. To investigate this question, we developed an experimental paradigm where all participants were administered category fluency tests across baseline, pleasant valence, unpleasant valence, and cognitive load conditions. Results. Compared to the non-schizotypy group, those with schizotypy performed significantly worse in pleasant and unpleasant valence conditions, but not cognitive load or baseline fluency tests. Conclusions. This study demonstrated the role of affect – but not cognitive load – on category fluency in psychometric schizotypy, as group differences only emerged once affective load was introduced. One explanation for this finding is that semantic memory may be unimpaired under normal conditions in psychometric schizotypy, but may be compromised once affective load is presented. Future studies should examine whether fluency deficits – particularly when affect is induced – predict future conversion to psychosis in psychometric schizotypy cohorts

Stigma Resistance is Positively Associated with Psychiatric and Psychosocial Outcomes: A Meta-analysis

To better understand how stigma resistance impacts functioning-related domains, we examined mean effect sizes between stigma resistance and: 1) symptoms (overall, positive, negative, and mood symptoms); 2) self-stigma; 3) self-efficacy; 4) quality of life; 5) recovery; 6) hope; 7) insight, and 8) overall outcomes (the average effect size across the constructs examined in each study). The mean effect size between stigma resistance and overall outcomes was significant and positive (r = 0.46, p < 0.001, k = 48). A large, negative effect size was found between stigma resistance and self-stigma (r = − 0.57, p < 0.001, k = 40). Large, positive effect sizes were found with self-efficacy (r = 0.60, p < 0.001, k = 25), quality of life (r = 0.51, p < 0.001, k = 17), hope (r = 0.54, p < 0.001, k = 8), and recovery (r = 0.60, p < 0.001, k = 7). Stigma resistance had a significant medium and small relationship with insight and symptoms, respectively. Race significantly moderated overall outcomes, self-stigma, mood symptoms, functioning, and hope associations. Education significantly moderated symptoms, functioning, and mood symptoms associations, and age significantly moderated self-stigma and negative symptom associations. Stigma resistance may be a key requirement for recovery. Individual characteristics influence resisting stigma and future work should prioritize cultural factors surrounding stigma resistance

A meta-analytic review of self-reported, clinician-rated, and performance-based motivation measures in schizophrenia: Are we measuring the same “stuff”?

An array of self-reported, clinician-rated, and performance-based measures has been used to assess motivation in schizophrenia; however, the convergent validity evidence for these motivation assessment methods is mixed. The current study is a series of meta-analyses that summarized the relationship between methods of motivation measurement in 45 studies of people with schizophrenia. The overall mean effect size between self-reported and clinician-rated motivation measures (r = 0.27, k = 33) was significant, positive, and approaching medium in magnitude, and the overall effect size between performance-based and clinician-rated motivation measures (r = 0.21, k = 11) was positive, significant, and small in magnitude. The overall mean effect size between self-reported and performance-based motivation measures was negligible and non-significant (r = −0.001, k = 2), but this meta-analysis was underpowered. Findings suggest modest convergent validity between clinician-rated and both self-reported and performance-based motivation measures, but additional work is needed to clarify the convergent validity between self-reported and performance-based measures. Further, there is likely more variability than similarity in the underlying construct that is being assessed across the three methods, particularly between the performance-based and other motivation measurement types. These motivation assessment methods should not be used interchangeably, and measures should be more precisely described as the specific motivational construct or domain they are capturing

Using Text-Analysis Computer Software and Thematic Analysis on the Same Qualitative Data: A Case Example

The acceptance and application of qualitative methods has been steadily increasing, and recent advances in computer analytic software programs have produced a rapidly evolving landscape of new methods and analytic tools. However, discussions regarding the use of these new computer-based methods alongside traditional qualitative methods remain sparse. The aim of this article is to present an example of using quantitative text analysis software, the Linguistic Inquiry and Word Count program, alongside a traditional qualitative method, thematic analysis. Data included 46 transcribed life-narratives shared by individuals with schizophrenia. We present findings from both analyses and offer an example of a method that combines these 2 approaches. Results and examples provided are discussed in light of the potential to strengthen analyses by using these methods collaboratively. (PsycINFO Database Record (c) 2017 APA, all rights reserved