Common Features in Compulsive Sexual Behavior, Substance Use Disorders, Personality, Temperament and Attachment—a Narrative Review

Do addictions share common traits of an “addictive personality” or do different addictions have distinct personality profiles? This narrative review examines the differences in the associations between substance use disorder (SUD) and compulsive sexual behavior disorder (CSBD), on the one hand, and personality traits, attachment dispositions, and temperament, on the other hand. We found that both people with a SUD and people with CSBD tended to be more spontaneous, careless, and less reliable, to place self-interest above getting along with others, to show emotional instability and experience negative emotions such as anger, anxiety, and/or depression, to be less able to control their attention and/or behavior, and to be engulfed with a constant sensation of “wanting”. Only people with CSBD, but not SUD, noted concerns with their social ties, fear of losing close others, and/or trusting others around them. Results also suggested that people with a SUD and people with CSBD share high commonalities in personality traits and temperament, yet there are noted differences in their social tendencies, especially with close others. People with CSBD reported more concerns with possible relationship losses compared to people with SUD issues, who may be more worried about losing their source of escapism

From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders

We present a constraint-based algorithm for learning causal structures from observational time-series data, in the presence of latent confounders. We assume a discrete-time, stationary structural vector autoregressive process, with both temporal and contemporaneous causal relations. One may ask if temporal and contemporaneous relations should be treated differently. The presented algorithm gradually refines a causal graph by learning long-term temporal relations before short-term ones, where contemporaneous relations are learned last. This ordering of causal relations to be learnt leads to a reduction in the required number of statistical tests. We validate this reduction empirically and demonstrate that it leads to higher accuracy for synthetic data and more plausible causal graphs for real-world data compared to state-of-the-art algorithms.Comment: Proceedings of the 40-th International Conference on Machine Learning (ICML), 202

Validity and reliability of computerized measurement of lumbar intervertebral disc height and volume from magnetic resonance images

BACKGROUND CONTEXT: Magnetic resonance (MR) examinations of morphologic characteristics of intervertebral discs (IVDs) have been used extensively for biomechanical studies and clinical investigations of the lumbar spine. Traditionally, the morphologic measurements have been performed using time- and expertise-intensive manual segmentation techniques not well suited for analyses of large-scale studies.