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Introduction: Numerous studies have highlighted cognitive benefits in lifelong bilinguals during aging, manifesting as superior performance on cognitive tasks compared to monolingual counterparts. Yet, the cognitive impacts of acquiring a new language in older adulthood remain unexplored. In this study, we assessed both behavioral and fMRI responses during a Stroop task in older adults, preand post language-learning intervention. Methods: A group of 41 participants (age:60-80) from a predominantly monolingual environment underwent a four-month online language course, selecting a new language of their preference. This intervention mandated engagement for 90 minutes a day, five days a week. Daily tracking was employed to monitor progress and retention. All participants completed a color-word Stroop task inside the scanner before and after the language instruction period. Results: We found that performance on the Stroop task, as evidenced by accuracy and reaction time, improved following the language learning intervention. With the neuroimaging data, we observed significant differences in activity between congruent and incongruent trials in key regions in the prefrontal and parietal cortex. These results are consistent with previous reports using the Stroop paradigm. We also found that the amount of time participants spent with the language learning program was related to differential activity in these brain areas. Specifically, we found that people who spent more time with the language learning program showed a greater increase in differential activity between congruent and incongruent trials after the intervention relative to before. Discussion: Future research is needed to determine the optimal parameters for language learning as an effective cognitive intervention for aging populations. We propose that with sufficient engagement, language learning can enhance specific domains of cognition such as the executive functions. These results extend the understanding of cognitive reserve and its augmentation through targeted interventions, setting a foundation for future investigations. [ABSTRACT FROM AUTHOR]

Background and purposeA number of therapies in development for patients with central nervous system injury aim to reduce disability by improving function of surviving brain elements rather than by salvaging tissue. The current study tested the hypothesis that, after adjusting for a number of clinical assessments, a measure of brain function at baseline would improve prediction of behavioral gains after treatment.MethodsTwenty-four patients with chronic stroke underwent baseline clinical and functional MRI assessments, received 6 weeks of rehabilitation therapy with or without investigational motor cortex stimulation, and then had repeat assessments. Thirteen baseline clinical/radiological measures were evaluated for ability to predict subsequent trial-related gains.ResultsAcross all patients, bivariate analyses found that greater trial-related functional gains were predicted by (1) smaller infarct volume, (2) greater baseline clinical status, and (3) lower degree of activation in stroke-affected motor cortex on baseline functional MRI. When these 3 variables were further assessed using multivariate linear regression modeling, only lower motor cortex activation and greater clinical status at baseline remained significant predictors. Note that lower baseline motor cortex activation was also associated with larger increases in motor cortex activation after treatment.ConclusionsLower motor cortex activity at baseline predicted greater behavioral gains after therapy, even after controlling for a number of clinical assessments. The boosts in cortical activity that paralleled behavioral gains suggest that in some patients, low baseline cortical activity represents underuse of surviving cortical resources. A measure of brain function might be important for optimal clinical decision-making in the context of a restorative intervention.

Objective: To evaluate the association between baseline cognitive function, intervention dropout, adherence and 3-month weight loss (WL) when controlling for confounding demographic variables. Methods: 107 (Mage = 40.9 yrs.), BMI in the overweight and obese range (BMI = 35.6 kg/m2), men (N = 17) and women (N = 90) completed a 3-month WL intervention. Participants attended weekly behavioral sessions, comply with a reduced calorie diet, and complete 100 min of physical activity (PA)/wk. Cognitive function tasks at baseline included Flanker (attention), Stroop (executive control) and working memory, demographics, body weight and cardiovascular fitness were assessed at baseline. Session attendance, adherence to PA and diet were recorded weekly. Results: Baseline attention was positively correlated with age (p <.05), education (p <.05), attendance (p <.05), diet (p <.05) and PA (p <.05). Baseline executive control (p <.05) and working memory (p <.05) were each associated with % WL. Baseline executive control (p <.01) and working memory (p <.001) were also each associated with education. ANOVA indicated that baseline attention (p <.01) was associated with WL, specifically for comparing those who achieved 5–10% WL (p <.01) and those who achieved greater than 10% WL (p <.01) to those who dropped. Significance: Results suggest that stronger baseline attention is associated with completion of a 3-mo. WL intervention. Executive control and working memory are associated with amount of WL achieved. NCT registration: US NIH Clinical Trials, NCT01664715 [ABSTRACT FROM AUTHOR]

Nonverbal strategic memory was evaluated in anorexia nervosa (AN) patients using a method previously applied to obsessive-compulsive (OC) spectrum disorders (OCD), as there seem to be similarities between AN and OC spectrum disorders. The study findings in OCD and BDD are similar, indicating important shared neuropsychological features among AN and these OC spectrum disorders.

A model for predicting cognitive change in a population of older adults is presented. The model considers cognitive factors such as demographic, physical and psychosocial variables and can accurately predict cognitive change within a 2- to 2.5-year period. An application of the model to a community consisting of 1,192 persons found that cognitive ability was directly influenced by education, strenuous activity, self-efficacy and peak pulmonary expiratory flow rate.