Behavioral/Systems/Cognitive Midbrain Dopamine Receptor Availability Is Inversely Associated with Novelty-Seeking Traits in Humans

Novelty-seeking personality traits are a major risk factor for the development of drug abuse and other unsafe behaviors. Rodent models of temperament indicate that high novelty responding is associated with decreased inhibitory autoreceptor control of midbrain dopamine neurons. It has been speculated that individual differences in dopamine functioning also underlie the personality trait of novelty seeking in humans. However, differences in the dopamine system of rodents and humans, as well as the methods for assessing novelty responding/seeking across species leave unclear to what extent the animal models inform our understanding of human personality. In the present study we examined the correlation between novelty-seeking traits in humans an

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

Heightened Attentional Capture by Threat in Veterans With PTSD

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Although an attentional bias for threat-relevant cues has been theorized in posttraumatic stress disorder (PTSD), to date empirical demonstration of this phenomenon has been at best inconsistent. Furthermore, the nature of this bias in PTSD has not been clearly delineated. In the present study, veterans with PTSD (n � 20), trauma-exposed veterans without PTSD (n � 16), and healthy nonveteran controls (n � 22) completed an emotional attentional blink task that measures the extent to which emotional stimuli capture and hold attention. Participants searched for a target embedded within a series of rapidly presented images. Critically, a combat-related, disgust, positive, or neutral distracter image appeared 200 ms, 400 ms, 600 ms, or 800 ms before the target. Impaired target detection was observed among veterans with PTSD relative to both veterans without PTSD and healthy nonveteran controls after only combat-related threat distracters when presented 200 ms, 400 ms, or 600 ms before the target, indicating increased attentional capture by cues of war and difficulty disengaging from such cues for an extended period. Veterans without PTSD and healthy nonveteran controls did not significantly differ from each other in target detection accuracy after combat-related threat distracters. These data support the presence of an attentional bias toward combat related stimuli in PTSD that should be a focus of treatment efforts

Incomplete hippocampal inversion: a neurodevelopmental mechanism for hippocampal shape deformation in schizophrenia

Background Shape analyses of patients with schizophrenia have revealed bilateral deformations of the anterolateral hippocampus, primarily localized to the CA1 subfield. Incomplete hippocampal inversion (IHI), an anatomical variant of the human hippocampus resulting from an arrest during neurodevelopment, is more prevalent and severe in patients with schizophrenia. We hypothesized that IHI would affect the shape of the hippocampus and contribute to hippocampal shape differences in schizophrenia. Methods We studied 199 schizophrenia patients and 161 healthy control participants with structural-MRI to measure the prevalence and severity of IHI. High-fidelity hippocampal surface reconstructions were generated with the SPHARM-PDM toolkit. We utilized general linear models in SurfStat to test for group shape differences, the impact of IHI on hippocampal shape variation, and whether IHI contributes to hippocampal shape abnormalities in schizophrenia. Results Not including IHI as a main effect in our between-group comparison replicated well-established hippocampal shape differences in schizophrenia patients localized to the CA1 subfield in the anterolateral hippocampus. Shape differences were also observed near the uncus and hippocampal tail. IHI was associated with outward displacements of the dorsal and ventral surfaces of the hippocampus and inward displacements of the medial and lateral surfaces. Including IHI as a main effect in our between-group comparison eliminated the bilateral shape differences in the CA1 subfield. Shape differences in the uncus persisted after including IHI. Conclusions IHI impacts hippocampal shape. Our results suggest IHI as a neurodevelopmental mechanism for the well-known shape differences, particularly in the CA1 subfield, in schizophrenia

Cognitive motor impairments and brain structure in schizophrenia spectrum disorder patients with a history of catatonia.

There is growing interest in understanding the behavioral and neural mechanisms of catatonia. Here, we examine cognition and brain structure in schizophrenia spectrum disorder (SSD) patients with a history of catatonia. A total of 172 subjects were selected from a data repository; these included SSD patients with (n = 43) and without (n = 43) a history of catatonia and healthy control subjects (n = 86). Cognitive functioning was assessed using the Screen for Cognitive Impairment in Psychiatry (SCIP) and brain structure was assessed using voxel-based morphometry (VBM) in the CAT12 toolbox. SSD patients with a history of catatonia showed worse performance on tests of verbal fluency and processing speed compared to SSD patients without such a history, even after controlling for current antipsychotic and benzodiazepine use. No differences were found between patients with and without a history of catatonia in terms of brain structure. Both patient groups combined showed significantly smaller grey matter volumes compared to healthy control subjects in brain regions consistent with prior studies, including the anterior cingulate, insular, temporal, and medial frontal cortices. The results highlight a cognitive-motor impairment in SSD patients with a history of catatonia. Challenges and limitations of examining brain structure in patients with a history of catatonia are discussed