BackgroundPTEN Hamartoma Tumor Syndrome (PHTS) is a rare syndrome with a broad phenotypic spectrum, including increased risks of breast (BC, 67%-78% at age 60 years), endometrial (EC, 19%-28%), and... Show moreBackgroundPTEN Hamartoma Tumor Syndrome (PHTS) is a rare syndrome with a broad phenotypic spectrum, including increased risks of breast (BC, 67%-78% at age 60 years), endometrial (EC, 19%-28%), and thyroid cancer (TC, 6%-38%). Current risks are likely overestimated due to ascertainment bias. We aimed to provide more accurate and personalized cancer risks.MethodsThis was a European, adult PHTS cohort study with data from medical files, registries, and/or questionnaires. Cancer risks and hazard ratios were assessed with Kaplan-Meier and Cox regression analyses, and standardized incidence ratios were calculated. Bias correction consisted of excluding cancer index cases and incident case analyses.ResultsA total of 455 patients were included, including 50.5% index cases, 372 with prospective follow-up (median 6 years, interquartile range = 3-10 years), and 159 of 281 females and 39 of 174 males with cancer. By age 60 years, PHTS-related cancer risk was higher in females (68.4% to 86.3%) than males (16.4% to 20.8%). Female BC risks ranged from 54.3% (95% confidence interval [CI] = 43.0% to 66.4%) to 75.8% (95% CI = 60.7% to 88.4%), with two- to threefold increased risks for PTEN truncating and approximately twofold for phosphatase domain variants. EC risks ranged from 6.4% (95% CI = 2.1% to 18.6%) to 22.1% (95% CI = 11.6% to 39.6%) and TC risks from 8.9% (95% CI = 5.1% to 15.3%) to 20.5% (95% CI = 11.3% to 35.4%). Colorectal cancer, renal cancer, and melanoma risks were each less than 10.0%.ConclusionsFemales have a different BC risk depending on their PTEN germline variant. PHTS patients are predominantly at risk of BC (females), EC, and TC. This should be the main focus of surveillance. These lower, more unbiased and personalized risks provide guidance for optimized cancer risk management. Show less
Resting state functional connectivity can be leveraged to investigate bilingual individual differences in cognitive control of language; however, thus far no report is provided on how the... Show moreResting state functional connectivity can be leveraged to investigate bilingual individual differences in cognitive control of language; however, thus far no report is provided on how the connectivity profiles of brain functional networks at rest point to different language control behavior in bilinguals. In order to address this gap in state-of-the-art research we did a functional connectivity analysis on the resting state data acquired via multiband EPI to investigate three resting state networks of interest namely, the frontoparietal network (FPN), the salience network (SN), and the default mode network (DMN), which are related to cognitive control, between two groups of Dutch–English bilinguals based on how they performed in a language switching task. Results demonstrated that there is the increased coupling of the left primary somatosensory cortex with the dorsolateral prefrontal cortex in the group with better performance in cognitive control of language and the increased coupling of the right primary somatosensory cortex with the inferior parietal cortex in the group with poorer performance in this executive function. As regards these results, we claim that the primary somatosensory cortex has a dual func- tion in coupling with the dorsolateral prefrontal cortex and the inferior parietal cortex in the FPN, and in fact, in what characterizes bilingual individual differences in cognitive control of language in healthy participants. The results of this study provide a model for future research in cognitive control of language and may serve as a ref- erence in clinical neuroscience when bilinguals are diagnosed with dysfunction in cognitive control. Show less
Botvinik-Nezer, R.; Holzmeister, F.; Camerer, C.F.; Dreber, A.; Huber, J.; Johannesson, M.; ... ; Schonberg, T. 2020
Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance... Show moreData analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses(1). The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset(2-5). Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses. Show less
Botvinik-Nezer, R.; Holzmeister, F.; Camerer, C.F.; Dreber, A.; Huber, J.; Johannesson, M.; ... ; Schonberg, T. 2020
Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance... Show moreData analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses(1). The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset(2-5). Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses. Show less
Botvinik-Nezer, R.; Holzmeister, F.; Camerer, C.F.; Dreber, A.; Huber, J.; Johannesson, M.; ... ; Schonberg, T. 2020
Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance... Show moreData analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses(1). The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset(2-5). Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses. Show less
