RESUMEN
Open science principles are revolutionizing the transparency, reproducibility, and accessibility of research. Meta-analysis has become a key technique for synthesizing data across studies in a principled way; however, its impact is contingent on adherence to open science practices. Here, we outline 9 quick tips for open meta-analyses, aimed at guiding researchers to maximize the reach and utility of their findings. We advocate for outlining preregistering clear protocols, opting for open tools and software, and the use of version control systems to ensure transparency and facilitate collaboration. We further emphasize the importance of reproducibility, for example, by sharing search syntax and analysis scripts, and discuss the benefits of planning for dynamic updating to enable living meta-analyses. We also recommend publication in open-access formats, as well as open data, open code, and open access publication. We close by encouraging active promotion of research findings to bridge the gap between complex syntheses and public discourse, and provide a detailed submission checklist to equip researchers, reviewers and journal editors with a structured approach to conducting and reporting open meta-analyses.
Asunto(s)
Metaanálisis como Asunto , Programas Informáticos , Humanos , Reproducibilidad de los Resultados , Proyectos de Investigación , Biología Computacional/métodosRESUMEN
Blood oxygen level dependent (BOLD) variability (SDBOLD) has emerged as a unique measure of the adaptive properties of neural systems that facilitate fast, stable responding, based on claims that SDBOLD is independent of mean BOLD signal (meanBOLD) and a powerful predictor of behavioral performance. We challenge these two claims. First, the apparent independence of SDBOLD and meanBOLD may reflect the presence of deactivations; we hypothesize that although SDBOLD may not be related to raw meanBOLD, it will be linearly related to "absolute" meanBOLD. Second, the observed relationship between SDBOLD and performance may be an artifact of using fixed-length trials longer than RTs. Such designs provide opportunities to toggle between on- and off-task states, and fast responders likely engage in more frequent state-switching, thereby artificially elevating SDBOLD. We hypothesize that SDBOLD will be higher and more strongly related to performance when using such fixed-length trials relative to self-paced trials that terminate upon a response. We test these two hypotheses in an fMRI study using blocks of fixed-length or self-paced trials. Results confirmed both hypotheses: (1) SDBOLD was robustly related with absolute meanBOLD, and (2) toggling between on- and off-task states during fixed-length trials reliably contributed to SDBOLD. Together, these findings suggest that a reappraisal of the functional significance of SDBOLD as a unique marker of cognitive performance is warranted.
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Physical exercise confers numerous benefits to brain structure, function and cognition, however, considerable individual variability exists in these effects. Emerging paradigms focused on intraindividual dynamics provide novel opportunities to map and leverage individualized neural architectures underlying exercise-cognition relationships. Progress at the intersection of psychometrics, structural and functional neuroimaging, electrophysiology, and genetics can be integrated to elucidate each individual's potential for improvement, as well as the specific abilities that are most likely to benefit from exercise regimens. These personalized profiles can then guide targeted exercise programs tailored to effectively modulate the pathways identified as most promising for that individual. Such mapping-guided exercise interventions tailored to a person's neurocognitive profile allows optimizing cognitive improvements compared to results elicited by generic regimens. While still in its infancy, precision interventions represent an innovative future direction to advance exercise in support of brain health, toward potent, truly personalized cognitive enhancement.