Cognitively Stimulating Activities: Effects on Cognition across Four Studies with up to 21 Years of Longitudinal Data

Engagement in cognitively stimulating activities has been considered to maintain or strengthen cognitive skills, thereby minimizing age-related cognitive decline. While the idea that there may be a modifiable behavior that could lower risk for cognitive decline is appealing and potentially empowering for older adults, research findings have not consistently supported the beneficial effects of engaging in cognitively stimulating tasks. Using observational studies of naturalistic cognitive activities, we report a series of mixed effects models that include baseline and change in cognitive activity predicting cognitive outcomes over up to 21 years in four longitudinal studies of aging. Consistent evidence was found for cross-sectional relationships between level of cognitive activity and cognitive test performance. Baseline activity at an earlier age did not, however, predict rate of decline later in life, thus not supporting the concept that engaging in cognitive activity at an earlier point in time increases one's ability to mitigate future age-related cognitive decline. In contrast, change in activity was associated with relative change in cognitive performance. Results therefore suggest that change in cognitive activity from one's previous level has at least a transitory association with cognitive performance measured at the same point in time

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Principles of Neuropsychological Interpretation

Interpretation of neuropsychological data is the process by which significance and meaning are derived from the information obtained during the evaluation process. In this regard, what comes to mind most readily is the interpretation of test scores obtained during assessment. Test scores in and of themselves have little meaning in isolation. However, when compared to some normative standard, test scores provide much information regarding how the individual performs relative to similarly aged peers, the extent to which that score deviates from the norm or average score, and the degree to which that score is likely to reflect spared or impaired abilities

Breadth Versus Depth: Balance Achieved

This text (see record 1996-97886-000) is likely to serve the needs of a relatively broad readership in its deliberate balance of general and specific information. In keeping with the force that guides most psychological reality, the authors have made an explicit choice to go with probability; that is, high probability, high base rate, common disorders encountered in neuropsychological practice. In doing so, the authors include those disorders encountered not only by neuropsychologists but also by clinical, counseling, and school psychologists as well as a host of other mental health and health-related professionals. In organizing the text around specific yet common disorders, the authors have opted to take a more general, overarching approach to each disorder that includes information on clinical characteristics, pathophysiology, and assessment and treatment issues. This provides the reader with a relatively comprehensive overview of the disorder in question. The text is written with a relatively broad audience in mind. A background in neuropsychology is not assumed. Furthermore, the approach is a practical one, intended to link neuropsychological assessment with the more general clinical features of the disorder as well as with its prognosis and treatment

Principles of Neuropsychological Interpretation

Interpretation of neuropsychological data is the process by which significance and meaning are derived from the information obtained during the evaluation process. In this regard, what comes to mind most readily is the interpretation of test scores obtained during assessment. Test scores in and of themselves have little meaning in isolation. However, when compared to some normative standard, test scores provide much information regarding how the individual performs relative to similarly aged peers, the extent to which that score deviates from the norm or average score, and the degree to which that score is likely to reflect spared or impaired abilities

Hemispheric Lateralization of Perception and Memory for Emotional Verbal Stimuli in Normal Individuals

The purposes of this study were to extend the literature on lateralization of perception of emotional verbal stimuli in normal individuals, including a test of both the right hemisphere and valence models, and to investigate predictions from these models regarding lateralization of memory for emotional verbal stimuli in normal individuals, an area that, to our knowledge, has not been investigated. Seventy-nine undergraduates were presented lateralized positive, negative, and neutral English words and nonwords. Participants were then asked to freely recall the presented words and, after a 20-min delay, to recognize the words. Recognition memory data provided strong support for the valence model. In addition, free-recall and perception data provided partial support for this model. The literature on the lateralization of processing emotional verbal and nonverbal material is discussed