Brain-age estimation with a low-cost EEG-headset: effectiveness and implications for large-scale screening and brain optimization.

Over time, pathological, genetic, environmental, and lifestyle factors can age the brain and diminish its functional capabilities. While these factors can lead to disorders that can be diagnosed and treated once they become symptomatic, often treatment is difficult or ineffective by the time significant overt symptoms appear. One approach to this problem is to develop a method for assessing general age-related brain health and function that can be implemented widely and inexpensively. To this end, we trained a machine-learning algorithm on resting-state EEG (RS-EEG) recordings obtained from healthy individuals as the core of a brain-age estimation technique that takes an individual's RS-EEG recorded with the low-cost, user-friendly EMOTIV EPOC X headset and returns that person's estimated brain age. We tested the current version of our machine-learning model against an independent test-set of healthy participants and obtained a correlation coefficient of 0.582 between the chronological and estimated brain ages (r = 0.963 after statistical bias-correction). The test-retest correlation was 0.750 (0.939 after bias-correction) over a period of 1 week. Given these strong results and the ease and low cost of implementation, this technique has the potential for widespread adoption in the clinic, workplace, and home as a method for assessing general brain health and function and for testing the impact of interventions over time.

Development and feasibility of a mindfulness-based dance/movement therapy intervention for chronic low back pain.

Introduction: Responding to the need for innovative, multi-modal, non-pharmacological strategies in chronic low back pain (cLBP) care, this article presents the development and a mixed methods feasibility trial of a manualized Mindfulness-based Dance/Movement Therapy (M-DMT) program for cLBP. The 12-week program is designed as a group therapy, integrating mindfulness principles, creative/expressive dance and movement, and psychoeducational content focused on cLBP management. This holistic program seeks to cultivate nonjudgmental awareness of pain experiences, challenge maladaptive pain-related beliefs, enhance emotional well-being, foster social support, and promote effective coping strategies for the daily challenges associated with cLBP. Methods: The 12-week M-DMT intervention was administered to individuals with non-specific cLBP (N = 18, aged 51.7 ± 13.9 years, 72% female, 55% Black and 39% White). We assessed feasibility and acceptability through monitoring enrollment and retention rates, attendance, and adverse events. Moreover, we measured the intervention's credibility/expectancy, participants' perception of changes, and overall satisfaction. Additionally, we collected qualitative data, capturing participants' perspectives on the intervention's usefulness and perceived benefits. Specific benchmarks were established to gauge the successful feasibility and acceptability of the program. Results: The adherence rate stood at 80%, with a perfect retention rate of 100%. The study successfully met the benchmarks for treatment acceptability and satisfaction criteria, with 61% of participants reporting "feeling better" or a "great deal better" after the intervention. No adverse events were observed. Participants found the intervention enjoyable and reported that it provided effective tools for cLBP and related symptoms. Notably, participants reported a decrease in fear-avoidance behaviors, increased motivation for physical activity, and a boost in self-efficacy for pain management. Discussion: These encouraging findings establish a strong basis for considering the M-DMT intervention as a promising approach for cLBP management, warranting further investigation in larger-scale studies.

Rationale and design of a single-center randomized trial to compare the graft patency between the radial artery and the no-touch saphenous vein in coronary artery bypass grafting surgery (GRAFT-CAB Study).

BACKGROUND: Previous studies suggested only the radial artery and the No-touch (NT) technique were effective in reducing graft occlusion after coronary artery bypass grafting (CABG) surgery. However, there is no randomized trial comparing these 2 graft conduits. The optimum second conduit for CABG remains undetermined. MATERIALS AND METHODS: This study is a prospective, single-center randomized clinical trial, aiming to compare the graft patency between the radial artery and the NT vein graft. All patients undergoing isolated CABG with left internal mammary artery (LIMA) plus at least 2 additional grafts will be considered eligible. About 774 cases (516 in the radial artery group and 258 in the NT vein group) will be enrolled in over 1 to 2 years. Participants will be randomized and allocated to two bypass strategies: the LIMA plus 1 radial artery and 1 conventional vein graft, or the LIMA plus 2 NT vein grafts. The primary outcome is graft occlusion at 1 year after CABG evaluated by CT angiography. The secondary outcomes include graft occlusion at 3 and 5 years and major adverse cardiac or cerebrovascular events at 1, 3, and 5 years follow-ups. DISCUSSION: This study will define whether or not the NT vein has a lower graft occlusion rate than the radial artery in short and mid-term follow-ups, and provide new evidence for the second conduit choice in CABG surgery. TRIAL REGISTRATION: ClinicalTrials.gov NCT06014047. Registered on October 15th, 2023.

STING agonist, SMA-2, inhibits clear cell renal cell carcinoma through improving tumor microenvironment.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent and lethal subtype of kidney cancer, patients with ccRCC usually have very poor prognosis and short survival. Therefore, it is urgent to develop more effective therapeutics or medications to suppress ccRCC progression. Here, we demonstrated that STING agonist, MSA-2 significantly inhibits tumor progress and prolongs the survival of ccRCC mice by promoting cytokines secretion. Moreover, MSA-2 triggered the trafficking and infiltration of CD8+ T cells, supported by the generation of a chemokine milieu that promoted recruitment and modulation of the immunosuppressive TME in ccRCC. These findings suggest that MSA-2 potentially serves an effective and preferable adjuvant immunotherapy of ccRCC.

Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study.

OBJECTIVE: Going extended periods of time without eating increases risk for binge eating and is a primary target of leading interventions for binge-spectrum eating disorders (B-EDs). However, existing treatments for B-EDs yield insufficient improvements in regular eating and subsequently, binge eating. These unsatisfactory clinical outcomes may result from limitations in assessment and promotion of regular eating in therapy. Detecting the absence of eating using passive sensing may improve clinical outcomes by facilitating more accurate monitoring of eating behaviours and powering just-in-time adaptive interventions. We developed an algorithm for detecting meal consumption (and extended periods without eating) using continuous glucose monitor (CGM) data and machine learning. METHOD: Adults with B-EDs (N = 22) wore CGMs and reported eating episodes on self-monitoring surveys for 2 weeks. Random forest models were run on CGM data to distinguish between eating and non-eating episodes. RESULTS: The optimal model distinguished eating and non-eating episodes with high accuracy (0.82), sensitivity (0.71), and specificity (0.94). CONCLUSIONS: These findings suggest that meal consumption and extended periods without eating can be detected from CGM data with high accuracy among individuals with B-EDs, which may improve clinical efforts to target dietary restriction and improve the field's understanding of its antecedents and consequences.

Creative flow as optimized processing: Evidence from brain oscillations during jazz improvisations by expert and non-expert musicians.

Using a creative production task, jazz improvisation, we tested alternative hypotheses about the flow experience: (A) that it is a state of domain-specific processing optimized by experience and characterized by minimal interference from task-negative default-mode network (DMN) activity versus (B) that it recruits domain-general task-positive DMN activity supervised by the fronto-parietal control network (FPCN) to support ideation. We recorded jazz guitarists' electroencephalograms (EEGs) while they improvised to provided chord sequences. Their flow-states were measured with the Core Flow State Scale. Flow-related neural sources were reconstructed using SPM12. Over all musicians, high-flow (relative to low-flow) improvisations were associated with transient hypofrontality. High-experience musicians' high-flow improvisations showed reduced activity in posterior DMN nodes. Low-experience musicians showed no flow-related DMN or FPCN modulation. High-experience musicians also showed modality-specific left-hemisphere flow-related activity while low-experience musicians showed modality-specific right-hemisphere flow-related deactivations. These results are consistent with the idea that creative flow represents optimized domain-specific processing enabled by extensive practice paired with reduced cognitive control.

Intraday relations between physical activity and energy intake among behavioral weight loss participants.

OBJECTIVE: Weight loss results from a negative energy balance, when energy intake (EI) is less than energy expended, e.g., from physical activity (PA). However, PA may impact energy balance beyond energy expenditure alone, through indirect effects on eating behavior. Yet, no research has examined if engaging in PA-a central component of most weight loss programs-is associated with same-day EI among individuals with overweight/obesity pursuing weight loss. METHOD: Adults (N = 101) with overweight/obesity in a weight loss program were prescribed a reduced-calorie diet and PA regimen (250 min of moderate-to-vigorous PA at midtreatment). For 3 weeks at midtreatment, PA and EI were measured via an accelerometer and self-monitoring app, respectively. Multilevel models examined within-person relations between PA and EI preceding PA ("pre-PA"), acutely following PA ("acute post-PA," the 2 hr following PA), in the time following the acute post-PA period ("remaining time in day"), and across entire PA days ("full-day"), relative to non-PA matched time periods. RESULTS: EI was higher in the pre-PA and acute post-PA periods. There were no reliable differences in EI during the remaining time in day nor across the full-day on PA days versus within-subject matched non-PA days. There also was insufficient evidence to suggest EI pre-PA, post-PA, or across entire PA days, relative to non-PA matched time periods, was associated with percent weight change. CONCLUSIONS: Findings suggest that engaging in PA was associated with different within-person EI patterns compared to non-PA days, though there was little evidence to support that these patterns relate to weight change. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Biological age and brain age in midlife: relationship to multimorbidity and mental health.

Biological age and brain age estimated using biological and neuroimaging measures have recently emerged as surrogate aging biomarkers shown to be predictive of diverse health outcomes. As aging underlies the development of many chronic conditions, surrogate aging biomarkers capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Our study investigates whether elevated biological age and brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using Gaussian processes regression and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of disease accumulation and their relationship with aging.

Sample size optimization for clinical trials using graphical approaches for multiplicity adjustment.

Graphical approach provides a useful framework for multiplicity adjustment in clinical trials with multiple endpoints. When designing a graphical approach, initial weight and transition probability for the endpoints are often assigned based on clinical importance. For example, practitioners may prefer putting more weights on some primary endpoints. The clinical preference can be formulated as a constrain in the sample size optimization problem. However, there has been a lack of theoretical guidance on how to specify initial weight and transition probability in a graphical approach to meet the clinical preference but at the same time to minimize the sample size needed for a power requirement. To fill this gap, we propose statistical methods to optimize sample size over initial weight and transition probability in a graphical approach under a common setting, which is to use marginal power for each endpoint in a trial design. Importantly, we prove that some of the commonly used graphical approaches such as putting all initial weights on one endpoint are suboptimal. Our methods are flexible, which can be used for both single-arm trials and randomized controlled trials with either continuous or binary or mixed types of endpoints. Additionally, we prove the existence of optimal solution where all marginal powers are placed exactly at the prespecified values, assuming continuity. Two hypothetical clinical trial designs are presented to illustrate the application of our methods under different scenarios. Results are first presented for a design with two endpoints and are further generalized to three or more endpoints. Our findings are helpful to guide the design of a graphical approach and the sample size calculation in clinical trials.

Preventing prefrontal dysfunction by tDCS modulates stress-induced creativity impairment in women: an fNIRS study.

Stress is a major external factor threatening creative activity. The study explored whether left-lateralized activation in the dorsolateral prefrontal cortex manipulated through transcranial direct current stimulation could alleviate stress-induced impairment in creativity. Functional near-infrared spectroscopy was used to explore the underlying neural mechanisms. Ninety female participants were randomly assigned to three groups that received stress induction with sham stimulation, stress induction with true stimulation (anode over the left and cathode over the right dorsolateral prefrontal cortex), and control manipulation with sham stimulation, respectively. Participants underwent the stress or control task after the transcranial direct current stimulation manipulation, and then completed the Alternative Uses Task to measure creativity. Behavioral results showed that transcranial direct current stimulation reduced stress responses in heart rate and anxiety. The functional near-infrared spectroscopy results revealed that transcranial direct current stimulation alleviated dysfunction of the prefrontal cortex under stress, as evidenced by higher activation of the dorsolateral prefrontal cortex and frontopolar cortex, as well as stronger inter-hemispheric and intra-hemispheric functional connectivity within the prefrontal cortex. Further analysis demonstrated that the cortical regulatory effect prevented creativity impairment induced by stress. The findings validated the hemispheric asymmetry hypothesis regarding stress and highlighted the potential for brain stimulation to alleviate stress-related mental disorders and enhance creativity.

Behavioral weight loss outcomes in individuals with binge-eating disorder: A meta-analysis.

OBJECTIVE: Binge-eating disorder (BED) is highly comorbid with obesity. Weight loss may benefit individuals with BED; however, these individuals are often excluded from behavioral weight loss interventions (BWLIs), and findings from BWLIs including participants with and without BED are mixed. To the authors' knowledge, this study represents the first meta-analysis of weight loss outcomes of individuals with and without BED in BWLIs, while adjusting for weight-influencing variables. Treatment dropout rates were also examined. METHODS: Electronic search engines and grey literature search methods were used to identify manuscripts published through December 2022 related to BWLIs and BED. Thirty manuscripts (BED n = 1519; 25 non-BED n = 6345) were included. RESULTS: A meta-regression found that individuals with BED lost less weight compared with individuals without BED (~1.4 kg; ~2.9 kg among studies without meal replacements), but they still lost ~8.1 kg at post-treatment. A random-effects model showed that BED diagnosis increased odds of treatment dropout by 50%. CONCLUSIONS: BWLIs produced robust weight loss in those with BED, but those with BED lost less weight and had higher risk of dropout compared with those without. Future research should aim to close the gap in weight loss outcomes and retention between those with and without BED.

Using Wearable Passive Sensing to Predict Binge Eating in Response to Negative Affect Among Individuals With Transdiagnostic Binge Eating: Protocol for an Observational Study.

BACKGROUND: Binge eating (BE), characterized by eating a large amount of food accompanied by a sense of loss of control over eating, is a public health crisis. Negative affect is a well-established antecedent for BE. The affect regulation model of BE posits that elevated negative affect increases momentary risk for BE, as engaging in BE alleviates negative affect and reinforces the behavior. The eating disorder field's capacity to identify moments of elevated negative affect, and thus BE risk, has exclusively relied on ecological momentary assessment (EMA). EMA involves the completion of surveys in real time on one's smartphone to report behavioral, cognitive, and emotional symptoms throughout the day. Although EMA provides ecologically valid information, EMA surveys are often delivered only 5-6 times per day, involve self-report of affect intensity only, and are unable to assess affect-related physiological arousal. Wearable, psychophysiological sensors that measure markers of affect arousal including heart rate, heart rate variability, and electrodermal activity may augment EMA surveys to improve accurate real-time prediction of BE. These sensors can objectively and continuously measure biomarkers of nervous system arousal that coincide with affect, thus allowing them to measure affective trajectories on a continuous timescale, detect changes in negative affect before the individual is consciously aware of them, and reduce user burden to improve data completeness. However, it is unknown whether sensor features can distinguish between positive and negative affect states, given that physiological arousal may occur during both negative and positive affect states. OBJECTIVE: The aims of this study are (1) to test the hypothesis that sensor features will distinguish positive and negative affect states in individuals with BE with >60% accuracy and (2) test the hypothesis that a machine learning algorithm using sensor data and EMA-reported negative affect to predict the occurrence of BE will predict BE with greater accuracy than an algorithm using EMA-reported negative affect alone. METHODS: This study will recruit 30 individuals with BE who will wear Fitbit Sense 2 wristbands to passively measure heart rate and electrodermal activity and report affect and BE on EMA surveys for 4 weeks. Machine learning algorithms will be developed using sensor data to distinguish instances of high positive and high negative affect (aim 1) and to predict engagement in BE (aim 2). RESULTS: This project will be funded from November 2022 to October 2024. Recruitment efforts will be conducted from January 2023 through March 2024. Data collection is anticipated to be completed in May 2024. CONCLUSIONS: This study is anticipated to provide new insight into the relationship between negative affect and BE by integrating wearable sensor data to measure affective arousal. The findings from this study may set the stage for future development of more effective digital ecological momentary interventions for BE. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47098.

Sharing digital self-monitoring data with others to enhance long-term weight loss: A randomized controlled trial.

BACKGROUND: Participants in behavioral weight loss (BWL) programs increasingly use digital tools to self-monitor weight, physical activity, and dietary intake. Data collected with these tools can be systematically shared with other parties in ways that might support behavior change. METHODS: Adults age 18 to 70 with overweight/obesity (BMI 27-50 kg/m2) will enroll in a remotely delivered, 24-month BWL program designed to produce and maintain a 10% weight loss. Participants will be asked to use a wireless body weight scale, wearable activity sensor, and dietary intake app daily. All participants will receive individual and group counseling, engage in text messaging with members of their group, and appoint a friend or family member to serve in a support role. A 2x2x2 factorial design will test the effects of three types of data sharing partnerships: 1) Coach Share: The behavioral coach will regularly view digital self-monitoring data and address data observations. 2) Group Share: Participants will view each other's self-monitoring data in small-group text messages. 3) Friend/Family Share: A friend or family member will view the participant's data via automated message. The primary outcome is weight loss at 24 months. Mediators and moderators of intervention effects will be tested. CONCLUSION: This study will provide a clear indication of whether data sharing can improve long-term weight loss. This study will be the first to discern the mechanisms of action through which each type of data sharing may be beneficial, and elucidate conditions under which the benefits of data sharing may be maximized.

The Role of Physical Activity in Long-term Weight Loss: 36-month Results From a Randomized Controlled Trial.

BACKGROUND: Physical activity (PA) may promote long-term weight loss, but facilitating high levels of PA in behavioral weight loss programs is challenging. PURPOSE: This study reports the 36-month follow-up of a behavioral weight loss trial that tested the efficacy of increasing the emphasis on PA during treatment and using traditional or acceptance-based therapy (ABT) for this purpose. We also examined the extent to which long-term weight loss differed by PA pattern and tested if individual differences in eating behavior moderated this relationship. METHODS: Participants (N = 320) were randomized to (1) standard behavioral weight loss treatment (BT), (2) BT with a focus on PA, or (3) ABT with a focus on PA. Weight loss and PA were measured at 24- and 36-month follow-up. RESULTS: There were no differences between conditions in weight loss or PA at 24 or 36 months. Participants consistently engaging in high PA experienced the greatest weight losses. The positive impact of PA on weight loss was more pronounced among those with low emotional eating and those who believed that exercise did not affect their appetite. CONCLUSIONS: Findings emphasize the difficulty of improving long-term PA among adults with overweight/obesity beyond what standard behavioral weight loss treatment achieves. This study highlights the need to develop new PA treatment strategies, and suggests that ABT for weight loss may be more effective when applied to eating behavior versus PA. Results also demonstrate the importance of addressing problematic eating behavior and cognitions to fully realize the benefits of PA for weight loss. CLINICAL TRIAL INFORMATION: ClinicalTrials.gov identifier: NCT02363010.

Application of machine learning to discover interactions predictive of dietary lapses.

The purpose of this study it to build a machine learning model to predict dietary lapses with comparable accuracy, sensitivity, and specificity to previous literature while recovering predictor interactions. The sample for the current study consisted of merged data from two separate studies of individuals with obesity/overweight (total N = 87). Participants completed six ecological momentary assessment surveys per day where they were asked about 16 risk factors of lapse and if they had lapsed from their dietary prescriptions since the previous survey. Alcohol consumption and self-efficacy were the most prevalent in the top 10 stable interactions. Alcohol consumption decreased the protective effect of self-efficacy, motivation, and planning. Higher planning predicted higher risk for lapse only when consuming alcohol. Low motivation, hunger, cravings, and lack of healthy food availability increased the protective effect of self-efficacy. Higher self-efficacy increased risk effect of positive mood and having recently eaten a meal on lapse. For individuals with lower levels of self-efficacy, planning increased the risk of lapse. Alcohol intake and self-efficacy interact with several variables to predict dietary lapses, and these interactions should be targeted in just-in-time adaptive interventions that deliver interventions for lapses.

Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial.

Gold standard behavioral weight loss (BWL) is limited by the availability of expert clinicians and high cost of delivery. The artificial intelligence (AI) technique of reinforcement learning (RL) is an optimization solution that tracks outcomes associated with specific actions and, over time, learns which actions yield a desired outcome. RL is increasingly utilized to optimize medical treatments (e.g., chemotherapy dosages), and has very recently started to be utilized by behavioral treatments. For example, we previously demonstrated that RL successfully optimized BWL by dynamically choosing between treatments of varying cost/intensity each week for each participant based on automatic monitoring of digital data (e.g., weight change). In that preliminary work, participants randomized to the AI condition required one-third the amount of coaching contact as those randomized to the gold standard condition but had nearly identical weight losses. The current protocol extends our pilot work and will be the first full-scale randomized controlled trial of a RL system for weight control. The primary aim is to evaluate the hypothesis that a RL-based 12-month BWL program will produce non-inferior weight losses to standard BWL treatment, but at lower costs. Secondary aims include testing mechanistic targets (calorie intake, physical activity) and predictors (depression, binge eating). As such, adults with overweight/obesity (N = 336) will be randomized to either a gold standard condition (12 months of weekly BWL groups) or AI-optimized weekly interventions that represent a combination of expert-led group, expert-led call, paraprofessional-led call, and automated message). Participants will be assessed at 0, 1, 6 and 12 months.

Optimizing digital health technologies to improve therapeutic skill use and acquisition alongside enhanced cognitive-behavior therapy for binge-spectrum eating disorders: Protocol for a randomized controlled trial.

OBJECTIVE: Adjunctive mobile health (mHealth) technologies offer promise for improving treatment response to enhanced cognitive-behavior therapy (CBT-E) among individuals with binge-spectrum eating disorders, but research on the key "active" components of these technologies has been very limited. The present study will use a full factorial design to (1) evaluate the optimal combination of complexity of two commonly used mHealth components (i.e., self-monitoring and microinterventions) alongside CBT-E and (2) test whether the optimal complexity level of these interventions is moderated by baseline self-regulation. Secondary aims of the present study include evaluating target engagement associated with each level of these intervention components and quantifying the component interaction effects (i.e., partially additive, fully additive, or synergistic effects). METHOD: Two hundred and sixty-four participants with binge-spectrum eating disorders will be randomized to six treatment conditions determined by the combination of self-monitoring condition (i.e., standard self-monitoring or skills monitoring) and microinterventions condition (i.e., no microinterventions, automated microinterventions, or just-in-time adaptive interventions) as an augmentation to 16 sessions of CBT-E. Treatment outcomes will be measured using the Eating Disorder Examination and compared by treatment condition using multilevel models. RESULTS: Results will clarify the "active" components in mHealth interventions for binge eating. DISCUSSION: The present study will provide critical insight into the efficacy of commonly used digital intervention components (i.e., skills monitoring and microinterventions) alongside CBT-E. Furthermore, results of this study may inform personalization of digital intervention intensity based on patient profiles of self-regulation. PUBLIC SIGNIFICANCE: This study will examine the relative effectiveness of commonly used components of application-based interventions as an augmentation to cognitive-behavioral therapy for binge eating. Findings from this study will inform the development of an optimized digital intervention for individuals with binge eating.

Functional connectivity strength and topology differences in social phobia adolescents with and without ADHD comorbidity.

Social phobia (SP) is associated with changes in functional connectivity strength and topology. However, reported changes have been heterogeneous due to small sample sizes, inconsistent methodologies, and comorbidities, such as attention-deficit/hyperactivity disorder (ADHD), which has a high comorbidity rate with SP. Furthermore, there are few studies looking at SP in an adolescent population, a critical period for the development of the social brain. This project focuses on functional connectivity strength and topological differences in social phobia patients with and without ADHD comorbidity. We examined resting-state functional MRI images from 158 subjects, including 36 SP participants without ADHD comorbidity, 60 SP participants with ADHD comorbidity, and 62 healthy controls, with an overall average age of 14.16. We used a data-driven approach to examine impaired functional connectivity in a whole-brain analysis and higher-order topological differences in functional brain networks. We identified changes in the cerebellum and default mode network in social phobia patients as a whole, with the presence of ADHD comorbidity affecting various subsystems of the default mode network. Social phobia functional connectivity networks resembled random graphs, and local connectivity patterns in the superior occipital gyrus were different due to ADHD comorbidity. These alterations may indicate impairments in self-related processing, imagery, mentalizing, and predictive processes. We then used these changes in a linear support vector machine to distinguish between each pair of groups and achieved prediction accuracy significantly above chance rates. Our study extends prior research by showing that functional connectivity changes exist at adolescence, which are affected by ADHD comorbidity. As such, these results offer a new perspective in examining neurobiological changes in SP patients.

Gamification and neurotraining to engage men in behavioral weight loss: Protocol for a factorial randomized controlled trial.

Over 70% of men are overweight, and most desire weight loss; however, men are profoundly underrepresented in weight loss programs. Gamification represents a novel approach to engaging men and may enhance efficacy through two means: (1) game-based elements (e.g., streaks, badges, team-based competition) to motivate weight control behaviors and (2) arcade-style "neurotraining" to enhance neurocognitive capacities to resist the temptation of unhealthy foods and more automatically select healthy foods. This study will use a 2 × 2 factorial design to examine the independent and combinatory efficacy of gamification and inhibitory control training (ICT). Men with overweight/obesity (N = 228) will receive a 12-month mobile weight loss program that incorporates behavioral weight loss strategies (e.g., self-monitoring, goal setting, stimulus control). Men will be randomly assigned to a non-gamified or gamified version, and an active or sham ICT. A game design company will create the program, with input from a male advisory panel. Aims of the project are to test whether a gamified (versus non-gamified) weight loss program and/or ICT (versus sham) promotes greater improvements in weight, diet, and physical activity; whether these treatment factors have combinatory or synergistic effects; to test whether postulated mechanisms of action (increased engagement, for gamification, and inhibitory control, for ICT) mediate treatment effects; and whether baseline gameplay frequency and implicit preferences for ICT-targeted foods moderate effects. It is hoped this study will contribute to improved mHealth programs for men and enhance our understanding of the impact of gamified elements and neurocognitive training on weight control.

Cost-effective Bi2WO6 for efficient degradation of rhodamine B and tetracycline.

The morphology-controlled synthesis of nanostructured photocatalysts by an environmentally friendly and low-cost method provides a feasible way to realize practical applications of photocatalysts. Herein, Bi2WO6 (BWO) nanophotocatalysts with mulberry shape, sheet-like, and round-cake morphologies have been successfully synthesized through a highly facile solvothermal process by simply adjusting the solvothermal temperature or utilizing selective addition of ethylene glycol as an orientation agent without using strong acids and bases and/or hazardous chemicals. The ratio of ethylene glycol and glacial acetic acid can affect the morphology and oxygen vacancy content of BWO, thereby influencing the photocatalytic performance. The photocatalytic activity of the as-prepared samples was evaluated by degradation of rhodamine B (RhB) and tetracycline under visible-light irradiation. The results indicated that all the BWO samples exhibited morphology-associated photocatalytic activity, and the sheet-like structure of BWO obtained via solvothermal treatment at 120 °C with ethylene glycol and glacial acetic acid ratio of 1:3 achieved the maximum specific surface area and possessed abundant oxygen vacancies, exhibiting outstanding photocatalytic activity for degradation of RhB and tetracycline. The degradation rate of RhB reached 100% within 20 min. To the best of our knowledge, this value is one of the most remarkable values for pristine BWO photocatalysts. Radical capture experiments demonstrated that hydroxyl radicals (·OH) play major roles compared with electrons (e-) and holes (h+) in the photocatalytic degradation process. A possible mechanism for the photocatalytic degradation of pollutants was proposed to better understand the reaction process. We believe that the more economical, efficient and greener methodology can provide guidance to develop highly efficient photocatalysts with favourable morphology and structure. Supplementary Information: The online version contains supplementary material available at 10.1007/s10854-022-09654-z.