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Decision making has been extensively studied in the context of economics and from a group perspective, but still little is known on individual decision making. Here we discuss the different cognitive processes involved in decision making and its associated neural substrates. The putative conductors in decision making appear to be the prefrontal cortex and the striatum. Impaired decision-making skills in various clinical populations have been associated with activity in the prefrontal cortex and in the striatum. We highlight the importance of strengthening the degree of integration of both cognitive and neural substrates in order to further our understanding of decision-making skills. In terms of cognitive paradigms, there is a need to improve the ecological value of experimental tasks that assess decision making in various contexts and with rewards; this would help translate laboratory learnings into real-life benefits. In terms of neural substrates, the use of neuroimaging techniques helps characterize the neural networks associated with decision making; more recently, ways to modulate brain activity, such as in the prefrontal cortex and connected regions (eg, striatum), with noninvasive brain stimulation have also shed light on the neural and cognitive substrates of decision making. Together, these cognitive and neural approaches might be useful for patients with impaired decision-making skills. The drive behind this line of work is that decision-making abilities underlie important aspects of wellness, health, security, and financial and social choices in our daily lives.
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http://dx.doi.org/10.31887/DCNS.2016.18.1/sfecteau | DOI Listing |
Knee Surg Relat Res
September 2025
Florida Orthopaedic Institute, Gainesville, FL, 32607, USA.
Background: A clear understanding of minimal clinically important difference (MCID) and substantial clinical benefit (SCB) is essential for effectively implementing patient-reported outcome measurements (PROMs) as a performance measure for total knee arthroplasty (TKA). Since not achieving MCID and SCB may reflect suboptimal surgical benefit, the primary aim of this study was to use machine learning to predict patients who may not achieve the threshold-based outcomes (i.e.
View Article and Find Full Text PDFGenome Biol
September 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
Background: Soil salinization represents a critical global challenge to agricultural productivity, profoundly impacting crop yields and threatening food security. Plant salt-responsive is complex and dynamic, making it challenging to fully elucidate salt tolerance mechanism and leading to gaps in our understanding of how plants adapt to and mitigate salt stress.
Results: Here, we conduct high-resolution time-series transcriptomic and metabolomic profiling of the extremely salt-tolerant maize inbred line, HLZY, and the salt-sensitive elite line, JI853.
J Assist Reprod Genet
September 2025
Department of Gynecology, Pingxiang Maternal and Child Health Hospital, PingXiang, Jiangxi, China.
Objective: This study aimed to identify key predictors of uterine fibroid (UF) recurrence following laparoscopic myomectomy (LM) in reproductive-age women and to construct a predictive nomogram to support individualized clinical decision-making.
Methods: This retrospective cohort study included 459 women who underwent LM. Recurrence of UFs and risk of recurrence were analyzed.
Bariatric surgery is an effective treatment for morbid obesity, but patient outcomes differ greatly because of a variety of phenotypes, comorbidities, and postoperative adherence. In bariatric care, artificial intelligence (AI) and machine learning (ML) are becoming revolutionary tools because traditional predictive models based on BMI and demographic variables are unable to account for these complexities. To put it simply, AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence.
View Article and Find Full Text PDFJ Gen Intern Med
September 2025
University of Colorado School of Medicine, 1890 N Revere Ct, Third Floor, Mail Stop F443, Aurora, CO, 80045, USA.
Background: The SHARE Approach Model and training curriculum was developed by the Agency for Healthcare Research and Quality (AHRQ) to teach clinicians practicing in diverse settings how to engage in more effective Shared Decision Making (SDM).
Objective: To determine the effectiveness of the SHARE Approach at improving SDM in practices located across Colorado, USA.
Design: A longitudinal study with pre- and post-intervention observations.