Impaired Adult Neurogenesis in the Dentate Gyrus of a Triple Transgenic Mouse Model of Alzheimer's Disease

It has become generally accepted that new neurones are added and integrated mainly in two areas of the mammalian CNS, the subventricular zone and the subgranular zone (SGZ) of the dentate gyrus (DG) of the hippocampus, which is of central importance in learning and memory. The newly generated cells display neuronal morphology, are able to generate action potentials and receive functional synaptic inputs, i.e. their properties are similar to those found in mature neurones. Alzheimer's disease (AD) is the primary and widespread cause of dementia and is an age-related, progressive and irreversible neurodegenerative disease that deteriorates cognitive functions. Here, we have used male and female triple transgenic mice (3xTg-AD) harbouring three mutant genes (β-amyloid precursor protein, presenilin-1 and tau) and their respective non-transgenic (non-Tg) controls at 2, 3, 4, 6, 9 and 12 months of age to establish the link between AD and neurogenesis. Using immunohistochemistry we determined the area density of proliferating cells within the SGZ of the DG, measured by the presence of phosphorylated Histone H3 (HH3), and their possible co-localisation with GFAP to exclude a glial phenotype. Less than 1% of the HH3 labeled cells co-localised with GFAP. Both non-Tg and 3xTg-AD showed an age-dependent decrease in neurogenesis. However, male 3xTg-AD mice demonstrated a further reduction in the production of new neurones from 9 months of age (73% decrease) and a complete depletion at 12 months, when compared to controls. In addition, female 3xTg-AD mice showed an earlier but equivalent decrease in neurogenesis at 4 months (reduction of 63%) with an almost inexistent rate at 12 months (88% decrease) compared to controls. This reduction in neurogenesis was directly associated with the presence of β-amyloid plaques and an increase in the number of β-amyloid containing neurones in the hippocampus; which in the case of 3xgTg females was directly correlated. These results suggest that 3xTg-AD mice have an impaired ability to generate new neurones in the DG of the hippocampus, the severity of which increases with age and might be directly associated with the known cognitive impairment observed from 6 months of age onwards . The earlier reduction of neurogenesis in females, from 4 months, is in agreement with the higher prevalence of AD in women than in men. Thus it is conceivable to speculate that a recovery in neurogenesis rates in AD could help to rescue cognitive impairment

Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions. © The Author(s) 2023.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]