Cognitive training and remediation interventions for substance use disorders: a Delphi consensus study

Aims: Substance use disorders (SUD) are associated with cognitive deficits that are not always addressed in current treatments, and this hampers recovery. Cognitive training and remediation interventions are well suited to fill the gap for managing cognitive deficits in SUD. We aimed to reach consensus on recommendations for developing and applying these interventions. Design, Setting and Participants: We used a Delphi approach with two sequential phases: survey development and iterative surveying of experts. This was an on-line study. During survey development, we engaged a group of 15 experts from a working group of the International Society of Addiction Medicine (Steering Committee). During the surveying process, we engaged a larger pool of experts (n = 54) identified via recommendations from the Steering Committee and a systematic review. Measurements: Survey with 67 items covering four key areas of intervention development: targets, intervention approaches, active ingredients and modes of delivery. Findings: Across two iterative rounds (98% retention rate), the experts reached a consensus on 50 items including: (i) implicit biases, positive affect, arousal, executive functions and social processing as key targets of interventions; (ii) cognitive bias modification, contingency management, emotion regulation training and cognitive remediation as preferred approaches; (iii) practice, feedback, difficulty-titration, bias modification, goal-setting, strategy learning and meta-awareness as active ingredients; and (iv) both addiction treatment work-force and specialized neuropsychologists facilitating delivery, together with novel digital-based delivery modalities. Conclusions: Expert recommendations on cognitive training and remediation for substance use disorders highlight the relevance of targeting implicit biases, reward, emotion regulation and higher-order cognitive skills via well-validated intervention approaches qualified with mechanistic techniques and flexible delivery options.Medical Research Future Fund (MRFF) MRF1141214National Health and Medical Research Council (NHMRC) of Australia GNT200946

Modeling the heat transfer by conduction of nanocellular polymers with bimodal cellular structures

Nanocellular polymers are a new generation of materials with the potential of being used as very efficient thermal insulators. It has been proved experimentally that these materials present the Knudsen effect, which strongly reduces the conductivity of the gas phase. There are theoretical equations to predict the thermal conductivity due to this Knudsen effect, but all the models consider an average cell size. In this work, we propose a model to predict the thermal conductivity due to the conduction mechanisms of nanocellular materials with bimodal cellular structures, that is, with two populations of cells, micro and nanocellular. The novelty of our work is to consider not only the average cell size, but the cell size distribution. The predictions of the model are compared with the experimental conductivity of two real bimodal systems based on poly(methyl methacrylate) (PMMA), and it is proved that this new model provides more accurate estimations of the conductivity than the models that do not consider the bimodality. Furthermore, this model could be applied to monomodal nanocellular polymers. In particular, for monomodal materials presenting a wide cell size distribution and at low densities, the model predicts important variations in comparison with the current models in the literature. This result indicates that the cell size distribution must be included in the estimations of the thermal conductivity of nanocellular polymer

Knowing What to Respond in the Future Does Not Cancel the Influence of Past Events

Everyday tasks seldom involve isolate actions but sequences of them. We can see whether previous actions influence the current one by exploring the response time to controlled sequences of stimuli. Specifically, depending on the response-stimulus temporal interval (RSI), different mechanisms have been proposed to explain sequential effects in two-choice serial response tasks. Whereas an automatic facilitation mechanism is thought to produce a benefit for response repetitions at short RSIs, subjective expectancies are considered to replace the automatic facilitation at longer RSIs, producing a cost-benefit pattern: repetitions are faster after other repetitions but they are slower after alternations. However, there is not direct evidence showing the impact of subjective expectancies on sequential effects. By using a fixed sequence, the results of the reported experiment showed that the repetition effect was enhanced in participants who acquired complete knowledge of the order. Nevertheless, a similar cost-benefit pattern was observed in all participants and in all learning blocks. Therefore, results of the experiment suggest that sequential effects, including the cost-benefit pattern, are the consequence of automatic mechanisms which operate independently of (and simultaneously with) explicit knowledge of the sequence or other subjective expectancies

Evaluación neuropsicológica en adicciones: guía clínica

The aim of this article is to provide rommendations for neuropsychological assessment in the con­text of addiction treatment. I propose key basic guidelines to conduct neuropsychological as­sessment in two contexts: (1) profiling of cognitive sequela as­sociated  with substance use; (2) prediction of treat­ment otcomes, in terms of retention and ad­herence to addiction treatment, and risk of relapse. I also discuss novel therapeutic approaches spurred by a neuropsychological understanding of substance use disorders. These recommendations are aimed to fos­ter the transition between neuroscientific discovery and clinical translation, by providing basic guidelines to incorporate neuropsychological tools in clinical practice with addicted clients. ResumenEl objetivo de este artículo de opinión es proporcionar pautas para la evaluación neuropsicológica en el contexto terapéutico de las adicciones. Se proponen guías básicas para la evaluación neuropsicológica en dos contextos: (1) la determinación de los perfiles neuropsicológicos de usuarios consumidores de drogas; (2) la predicción de los resultados del tratamiento de las adicciones, en términos de retención y adherencia a las recomendaciones terapéuticas y de riesgo de recaídas en el consumo. Se describen también nuevas aproximaciones terapéuticas derivadas de una comprensión neuropsicológica de los trastornos por uso de sustancias. Las pautas recogidas en este artículo pretenden servir para facilitar la transición entre el descubrimiento científico y la implementación asistencial, proporcionando guías básicas para incorporar estas herramientas en la práctica clínica con pacientes drogodependientes.AbstractThe aim of this article is to provide rommendations for neuropsychological assessment in the con­text of addiction treatment. I propose key basic guidelines to conduct neuropsychological as­sessment in two contexts: (1) profiling of cognitive sequela as­sociated  with substance use; (2) prediction of treat­ment otcomes, in terms of retention and ad­herence to addiction treatment, and risk of relapse. I also discuss novel therapeutic approaches spurred by a neuropsychological understanding of substance use disorders. These recommendations are aimed to fos­ter the transition between neuroscientific discovery and clinical translation, by providing basic guidelines to incorporate neuropsychological tools in clinical practice with addicted clients.

Regularized multivariate analysis framework for interpretable high-dimensional variable selection

Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction which exploit correlations among input variables representing the data. One important property that is enjoyed by most such methods is uncorrelation among the extracted features. Recently, regularized versions of MVA methods have appeared in the literature, mainly with the goal to gain interpretability of the solution. In these cases, the solutions can no longer be obtained in a closed manner, and more complex optimization methods that rely on the iteration of two steps are frequently used. This paper recurs to an alternative approach to solve efficiently this iterative problem. The main novelty of this approach lies in preserving several properties of the original methods, most notably the uncorrelation of the extracted features. Under this framework, we propose a novel method that takes advantage of the,2,1 norm to perform variable selection during the feature extraction process. Experimental results over different problems corroborate the advantages of the proposed formulation in comparison to state of the art formulations.This work has been partly supported by MINECO projects TEC2013-48439-C4-1-R, TEC2014-52289-R and TEC2016-75161-C2-2-R, and Comunidad de Madrid projects PRICAM P2013/ICE-2933 and S2013/ICE-2933

Nonnegative OPLS for supervised design of filter banks: application to image and audio feature extraction

Audio or visual data analysis tasks usually have to deal with high-dimensional and nonnegative signals. However, most data analysis methods suffer from overfitting and numerical problems when data have more than a few dimensions needing a dimensionality reduction preprocessing. Moreover, interpretability about how and why filters work for audio or visual applications is a desired property, especially when energy or spectral signals are involved. In these cases, due to the nature of these signals, the nonnegativity of the filter weights is a desired property to better understand its working. Because of these two necessities, we propose different methods to reduce the dimensionality of data while the nonnegativity and interpretability of the solution are assured. In particular, we propose a generalized methodology to design filter banks in a supervised way for applications dealing with nonnegative data, and we explore different ways of solving the proposed objective function consisting of a nonnegative version of the orthonormalized partial least-squares method. We analyze the discriminative power of the features obtained with the proposed methods for two different and widely studied applications: texture and music genre classification. Furthermore, we compare the filter banks achieved by our methods with other state-of-the-art methods specifically designed for feature extraction.This work was supported in parts by the MINECO projects TEC2013-48439-C4-1-R, TEC2014-52289-R, TEC2016-75161-C2-1-R, TEC2016-75161-C2-2-R, TEC2016-81900-REDT/AEI, and PRICAM (S2013/ICE-2933)

The evidential value of research on cognitive training to change food-related biases and unhealthy eating behavior: A systematic review and p-curve analysis

Cognitive bias modification (CBM), which retrains implicit biases towards unhealthy foods, has been proposed as a promising adjunct to improve the efficacy of weight loss interventions. We conducted a systematic review of research on three CBM approaches (i.e., cue-specific inhibitory control, approach bias modification, and attentional bias modification) for reducing unhealthy eating biases and behavior. We performed a p-curve analysis to determine the evidential value of this research; this method is optimally suited to clarify whether published results reflect true effects or false positives due to publication and reporting biases. When considering all CBM approaches, our results suggested that the findings of CBM trials targeting unhealthy eating are unlikely to be false positives. However, only research on attentional bias modification reached acceptable levels of power. These results suggest that CBM interventions may be an effective strategy to enhance the efficacy of weight loss interventions. However, there is room for improvement in the methodological standards of this area of research, especially increasing the statistical power can help to fully clarify the clinical potential of CBM, and determine the role of potential moderatorsConsejería de Educación e Investigación, Grant/Award Numbers: 2016-T1/SOC-1395, 2020-5A/SOC-19723; Spanish Ministry of Science and Innovation, Grant/Award Numbers: PSI2017-85159-P, Ref. FJC2018-036047-

Impulsivity and body fat accumulation are linked to cortical and subcortical brain volumes among adolescents and adults

Obesity is associated not only with metabolic and physical health conditions, but with individual variations in cognition and brain health. This study examined the association between body fat (an index of excess weight severity), impulsivity (a vulnerability factor for obesity), and brain structure among adolescents and adults across the body mass index (BMI) spectrum. We used 3D T1 weighted anatomic magnetic resonance imaging scans to map the association between body fat and volumes in regions associated with obesity and impulsivity. Participants were 127 individuals (BMI: 18–40 kg/m2; M = 25.69 ± 5.15), aged 14 to 45 years (M = 24.79 ± 9.60; female = 64). Body fat was measured with bioelectric impendence technology, while impulsivity was measured with the UPPS-P Impulsive Behaviour Scale. Results showed that higher body fat was associated with larger cerebellar white matter, medial orbitofrontal cortex (OFC), and nucleus accumbens volume, although the latter finding was specific to adolescents. The relationship between body fat and medial OFC volume was moderated by impulsivity. Elevated impulsivity was also associated with smaller amygdala and larger frontal pole volumes. Our findings link vulnerability and severity markers of obesity with neuroanatomical measures of frontal, limbic and cerebellar structures, and unravel specific links between body fat and striatal volume in adolescence

Sparse and kernel OPLS feature extraction based on eigenvalue problem solving

Orthonormalized partial least squares (OPLS) is a popular multivariate analysis method to perform supervised feature extraction. Usually, in machine learning papers OPLS projections are obtained by solving a generalized eigenvalue problem. However, in statistical papers the method is typically formulated in terms of a reduced-rank regression problem, leading to a formulation based on a standard eigenvalue decomposition. A first contribution of this paper is to derive explicit expressions for matching the OPLS solutions derived under both approaches and discuss that the standard eigenvalue formulation is also normally more convenient for feature extraction in machine learning. More importantly, since optimization with respect to the projection vectors is carried out without constraints via a minimization problem, inclusion of penalty terms that favor sparsity is straightforward. In the paper, we exploit this fact to propose modified versions of OPLS. In particular, relying on the ℓ1 norm, we propose a sparse version of linear OPLS, as well as a non-linear kernel OPLS with pattern selection. We also incorporate a group-lasso penalty to derive an OPLS method with true feature selection. The discriminative power of the proposed methods is analyzed on a benchmark of classification problems. Furthermore, we compare the degree of sparsity achieved by our methods and compare them with other state-of-the-art methods for sparse feature extraction.This work was partly supported by MINECO projects TEC2011-22480 and PRIPIBIN-2011-1266.Publicad