Why the idea of framework propositions cannot contribute to an understanding of delusions

One of the tasks that recent philosophy of psychiatry has taken upon itself is to extend the range of understanding to some of those aspects of psychopathology that Jaspers deemed beyond its limits. Given the fundamental difficulties of offering a literal interpretation of the contents of primary delusions, a number of alternative strategies have been put forward including regarding them as abnormal versions of framework propositions described by Wittgenstein in On Certainty. But although framework propositions share some of the apparent epistemic features of primary delusions, their role in partially constituting the sense of inquiry rules out their role in helping to understand delusions

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe

Subcortical brain volume, regional cortical thickness, and cortical surface area across disorders: findings from the ENIGMA ADHD, ASD, and OCD Working Groups

Objective Attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. We aimed to directly compare all three disorders. The ENIGMA consortium is ideally positioned to investigate structural brain alterations across these disorders. Methods Structural T1-weighted whole-brain MRI of controls (n=5,827) and patients with ADHD (n=2,271), ASD (n=1,777), and OCD (n=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. We examined subcortical volume, cortical thickness and surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults using linear mixed-effects models adjusting for age, sex and site (and ICV for subcortical and surface area measures). Results We found no shared alterations among all three disorders, while shared alterations between any two disorders did not survive multiple comparisons correction. Children with ADHD compared to those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller ICV than controls and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared to adult controls and other clinical groups. No OCD-specific alterations across different age-groups and surface area alterations among all disorders in childhood and adulthood were observed. Conclusion Our findings suggest robust but subtle alterations across different age-groups among ADHD, ASD, and OCD. ADHD-specific ICV and hippocampal alterations in children and adolescents, and ASD-specific cortical thickness alterations in the frontal cortex in adults support previous work emphasizing neurodevelopmental alterations in these disorders

Automated multiscale vessel analysis for the quantification of MR angiography of peripheral arteriogenesis

Purpose: To automatically analyze the time course of collateralization in a rat hindlimb ischemia model based on signal intensity distribution (SID). Materials and Methods: Time-of-flight magnetic resonance angiograms (TOF-MRA) were acquired in eight rats at 2, 7, and 21 days after unilateral femoral artery ligation. Analysis was performed on maximum intensity projections filtered with multiscale vessel enhancement filter. Differences in SID between ligated limb and a reference region were monitored over time and compared to manual collateral artery identification. Results: The differences in SID correlated well with the number of collateral arteries found with manual quantification. The time courses of ultrasmall (diamete

Deep Learning Synthesis of White-Blood From Dark-Blood Late Gadolinium Enhancement Cardiac Magnetic Resonance

OBJECTIVES: Dark-blood late gadolinium enhancement (DB-LGE) cardiac magnetic resonance has been proposed as an alternative to standard white-blood LGE (WB-LGE) imaging protocols to enhance scar-to-blood contrast without compromising scar-to-myocardium contrast. In practice, both DB and WB contrasts may have clinical utility, but acquiring both has the drawback of additional acquisition time. The aim of this study was to develop and evaluate a deep learning method to generate synthetic WB-LGE images from DB-LGE, allowing the assessment of both contrasts without additional scan time.MATERIALS AND METHODS: DB-LGE and WB-LGE data from 215 patients were used to train 2 types of unpaired image-to-image translation deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation, with 5 different loss function hyperparameter settings each. Initially, the best hyperparameter setting was determined for each model type based on the Fréchet inception distance and the visual assessment of expert readers. Then, the CycleGAN and contrastive unpaired translation models with the optimal hyperparameters were directly compared. Finally, with the best model chosen, the quantification of scar based on the synthetic WB-LGE images was compared with the truly acquired WB-LGE.RESULTS: The CycleGAN architecture for unpaired image-to-image translation was found to provide the most realistic synthetic WB-LGE images from DB-LGE images. The results showed that it was difficult for visual readers to distinguish if an image was true or synthetic (55% correctly classified). In addition, scar burden quantification with the synthetic data was highly correlated with the analysis of the truly acquired images. Bland-Altman analysis found a mean bias in percentage scar burden between the quantification of the real WB and synthetic white-blood images of 0.44% with limits of agreement from -10.85% to 11.74%. The mean image quality of the real WB images (3.53/5) was scored higher than the synthetic white-blood images (3.03), P = 0.009.CONCLUSIONS: This study proposed a CycleGAN model to generate synthetic WB-LGE from DB-LGE images to allow assessment of both image contrasts without additional scan time. This work represents a clinically focused assessment of synthetic medical images generated by artificial intelligence, a topic with significant potential for a multitude of applications. However, further evaluation is warranted before clinical adoption.</p

MR Angiography of Collateral Arteries in a Hind Limb Ischemia Model: Comparison between Blood Pool Agent Gadomer and Small Contrast Agent Gd-DTPA

The objective of this study was to compare the blood pool agent Gadomer with a small contrast agent for the visualization of ultra-small, collateral arteries (diameter<1 mm) with high resolution steady-state MR angiography (SS-MRA) in a rabbit hind limb ischemia model. Ten rabbits underwent unilateral femoral artery ligation. On days 14 and 21, high resolution SS-MRA (voxel size 0.49×0.49×0.50 mm(3)) was performed on a 3 Tesla clinical system after administration of either Gadomer (dose: 0.10 mmol/kg) or a small contrast agent (gadopentetate dimeglumine (Gd-DTPA), dose: 0.20 mmol/kg). All animals received both contrast agents on separate days. Selective intra-arterial x-ray angiograms (XRAs) were obtained in the ligated limb as a reference. The number of collaterals was counted by two independent observers. Image quality was evaluated with the contrast-to-noise ratio (CNR) in the femoral artery and collateral arteries. CNR for Gadomer was higher in both the femoral artery (Gadomer: 73±5 (mean ± SE); Gd-DTPA: 40±3; p<0.01) and collateral arteries (Gadomer: 18±4; Gd-DTPA: 9±1; p = 0.04). Neither day of acquisition nor contrast agent used influenced the number of identified collateral arteries (p = 0.30 and p = 0.14, respectively). An average of 4.5±1.0 (day 14, mean ± SD) and 5.3±1.2 (day 21) collaterals was found, which was comparable to XRA (5.6±1.7, averaged over days 14 and 21; p>0.10). Inter-observer variation was 24% and 18% for Gadomer and Gd-DTPA, respectively. In conclusion, blood pool agent Gadomer improved vessel conspicuity compared to Gd-DTPA. Steady-state MRA can be considered as an excellent non-invasive alternative to intra-arterial XRA for the visualization of ultra-small collateral arteries

Estimating Surgical Urethral Length on Intraoperative Robot-Assisted Prostatectomy Images using Artificial Intelligence Anatomy Recognition

Objective: To construct a convolutional neural network (CNN) model that can recognize and delineate anatomic structures on intraoperative video frames of robot-assisted radical prostatectomy (RARP) and to use these annotations to predict the surgical urethral length (SUL). Background: Urethral dissection during RARP impacts patient urinary incontinence (UI) outcomes, and requires extensive training. Large differences exist between incontinence outcomes of different urologists and hospitals. Also, surgeon experience and education are critical toward optimal outcomes. Therefore, new approaches are warranted. SUL is associated with UI. Artificial intelligence (AI) surgical image segmentation using a CNN could automate SUL estimation and contribute toward future AI-assisted RARP and surgeon guidance. Methods: Eighty-eight intraoperative RARP videos between June 2009 and September 2014 were collected from a single center. Two hundred sixty-four frames were annotated according to prostate, urethra, ligated plexus, and catheter. Thirty annotated images from different RARP videos were used as a test data set. The dice (similarity) coefficient (DSC) and 95th percentile Hausdorff distance (Hd95) were used to determine model performance. SUL was calculated using the catheter as a reference. Results: The DSC of the best performing model were 0.735 and 0.755 for the catheter and urethra classes, respectively, with a Hd95 of 29.27 and 72.62, respectively. The model performed moderately on the ligated plexus and prostate. The predicted SUL showed a mean difference of 0.64 to 1.86 mm difference vs human annotators, but with significant deviation (standard deviation = 3.28-3.56). Conclusion: This study shows that an AI image segmentation model can predict vital structures during RARP urethral dissection with moderate to fair accuracy. SUL estimation derived from it showed large deviations and outliers when compared with human annotators, but with a small mean difference (<2 mm). This is a promising development for further research on AI-assisted RARP

Responsive biomimetic networks from polyisocyanopeptide hydrogels

Contains fulltext : 111406.pdf (preprint version ) (Open Access) Contains fulltext : 111406_suppl.pdf (preprint version ) (Open Access