This systematic review aims to examine growing evidence linking cognitive-executive functions with addiction treatment outcomes, and to discuss significant cognitive predictors drawing upon addiction neuroscience theory. We conducted a systematic search to identify studies using measures of general cognition and executive functions in patients with substance use disorders for the purpose of predicting two treatment outcomes: therapeutic adherence and relapse. Forty-six studies were selected, and sample characteristics, timing of assessments, and cognitive measures were analyzed. We observed significant methodological differences across studies, resulting in substantial variability in the relationships between cognitive-executive domains and treatment outcomes. Notwithstanding this variability, we found evidence of associations, of medium effect size, between general cognition and treatment adherence, and between reward-based decision-making and relapse. The link between general cognition and treatment adherence is consistent with emerging evidence linking limited cognitive-executive resources with less ability to benefit from talk therapies. The link between reward-based decision-making and relapse accords with decision neuroscience models of addiction. Findings may inform preclinical and clinical research concerning addiction treatment mechanisms.
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http://dx.doi.org/10.1016/j.neubiorev.2016.09.030 | DOI Listing |
Psychol Serv
January 2025
Center for Health Equity Research and Promotion, Department of Veterans Affairs Pittsburgh Healthcare System.
Chronic insomnia is one of the most common health problems among veterans and can significantly impact health, function, and quality of life. Brief behavioral treatment for insomnia (BBTI), an adaptation of cognitive behavioral therapy for insomnia (CBT-I), was developed to help increase access to care outside of specialty settings. However, training providers alone is rarely sufficient, and implementation strategies are needed for successful uptake, adoption, and sustainable delivery of care.
View Article and Find Full Text PDFWorld J Pediatr
January 2025
The First Hospital of Peking University, Beijing, China.
Background: Glucose transporter 1 deficiency syndrome (Glut1DS) was initially reported by De Vivo and colleagues in 1991. This disease arises from mutations in the SLC2A1 and presents with a broad clinical spectrum. It is a treatable neuro-metabolic condition, where prompt diagnosis and initiation of ketogenic dietary therapy can markedly enhance the prognosis.
View Article and Find Full Text PDFJ Clin Invest
January 2025
Growth, Development, and Mental Health of Children and Adolescence Center, Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.
Posttranslational modification (PTM) of the amyloid precursor protein (APP) plays a critical role in Alzheimer's disease (AD). Recent evidence reveals that lactylation modification, as a novel PTM, is implicated in the occurrence and development of AD. However, whether and how APP lactylation contributes to both the pathogenesis and cognitive function in AD remains unknown.
View Article and Find Full Text PDFStroke
January 2025
Center for Brain Recovery, Boston University, MA (M.J.M., E.C., M.S., M.R.-M., S.K.).
Background: Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment planning. This study evaluated machine learning models to predict TLI and CLG and identified the key predictive features (eg, patient severity, demographics, and treatment variables) aligning with clinical evidence.
Methods: Forty-eight Spanish-English bilingual individuals with poststroke aphasia received 20 sessions of semantic feature-based naming treatment in either their first or second language.
J Chem Inf Model
January 2025
Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions.
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