Psychopathy is a personality disorder with strong links to criminal behavior. While research on psychopathy has focused largely on socio-affective dysfunction, recent data suggest that aberrant decision making may also play an important role. Yet, the circuit-level mechanisms underlying maladaptive decision making in psychopathy remain unclear. Here, we used a multi-modality functional imaging approach to identify these mechanisms in a population of adult male incarcerated offenders. Psychopathy was associated with stronger subjective value-related activity within the nucleus accumbens (NAcc) during inter-temporal choice and with weaker intrinsic functional connectivity between NAcc and ventromedial prefrontal cortex (vmPFC). NAcc-vmPFC connectivity strength was negatively correlated with NAcc subjective value-related activity; however, this putative regulatory pattern was abolished as psychopathy severity increased. Finally, weaker cortico-striatal regulation predicted more frequent criminal convictions. These data suggest that cortico-striatal circuit dysregulation drives maladaptive decision making in psychopathy, supporting the notion that reward system dysfunction comprises an important neurobiological risk factor.
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http://dx.doi.org/10.1016/j.neuron.2017.06.030 | DOI Listing |
BMC Med Educ
January 2025
Deakin Optometry, School of Medicine, Deakin University, 75 Pigdons Road, Waurn Ponds , VIC, 3216, Australia.
Background: Clinical reasoning is a professional capability required for clinical practice. In preclinical training, clinical reasoning is often taught implicitly, and feedback is focused on discrete outcomes of decision-making. This makes it challenging to provide meaningful feedback on the often-hidden metacognitive process of reasoning to address specific clinical reasoning difficulties.
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January 2025
Machine Learning Lab, University of Freiburg, Freiburg, Germany.
Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories, gradient-boosted decision trees have dominated tabular data for the past 20 years.
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January 2025
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Clinical decision-making is driven by multimodal data, including clinical notes and pathological characteristics. Artificial intelligence approaches that can effectively integrate multimodal data hold significant promise in advancing clinical care. However, the scarcity of well-annotated multimodal datasets in clinical settings has hindered the development of useful models.
View Article and Find Full Text PDFNat Neurosci
January 2025
Brain Research Institute, University of Zurich, Zurich, Switzerland.
Appropriate risk evaluation is essential for survival in complex, uncertain environments. Confronted with choosing between certain (safe) and uncertain (risky) options, animals show strong preference for either option consistently across extended time periods. How such risk preference is encoded in the brain remains elusive.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurology, The Second Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian City, 116023, Liaoning, China.
To develop and validate practical prediction tools to estimate poor outcomes in patients ≥ 80 years old with acute ischemic stroke after intravenous alteplase thrombolysis, aiding clinical decision-making.To explore the longest benefit window after thrombolysis in the elderly. 1: A retrospectively analysis was conducted on acute stroke patients who underwent intravenous thrombolysis.
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