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Tau positron emission tomography (PET) is increasing in popularity for biomarker characterization of Alzheimer’s disease (AD), and recent frameworks rely on tau PET cut-points to stage individuals along the AD continuum. Given the lack of standardization in tau PET thresholding methods, this study sought to systematically canvass and characterize existing studies that have derived tau PET cut-points and then directly assess different methods of tau PET thresholding in terms of their concurrent validity.

First, a literature search was conducted in PubMed to identify studies of AD and related clinical phenotypes that used the Flortaucipir (AV-1451) tau PET tracer to derive a binary cut-point for tau positivity. Of 540 articles screened and 47 full-texts reviewed, 23 cohort studies met inclusion criteria with a total of 6536 participants. Second, we derived and compared tau PET cut-points in a 2 × 2 × 2 design that systematically varied region (temporal meta-ROI and entorhinal cortex), analytic method (receiver operating characteristics and 2 standard deviations above comparison group), and criterion/comparison variable (amyloid-beta negative cognitively unimpaired or cognitively unimpaired only) using a sample of 453 older adults from the Alzheimer’s Disease Neuroimaging Initiative.

For the systematic review, notable variability in sample characteristics, preprocessing methods, region of interest, and analytic approach were observed, which were accompanied by discrepancy in proposed tau PET cut points. The empirical follow-up indicated the cut-point derived based on 2 standard deviations above a either comparison group in either ROI best differentiated tau positive and negative groups on cerebrospinal fluid phosphorylated tau, Mini-Mental State Examination score, and delayed memory performance.

Given the impact of discrepant thresholds on tau positivity rates, biomarker staging, and eligibility for future clinical treatment trials, recommendations are offered to select cut-point derivations based on the unique goals and priorities of different studies.

 

Male orangutans exhibit bimaturism—two mature morphs—flanged and unflanged males. Flanged males are larger, have cheek pads (flanges) and large throat sacs, and produce long calls. Previous orangutan paternity studies found variation between the reproductive success of each morph and in the degrees of reproductive skew. However, these studies were limited by a lack of behavioral maternity data, the inclusion of ex-captive orangutans, and/or the presence of feeding stations. Here we present the first paternity data from completely wild orangutans with known mothers. We hypothesized that (1) flanged males would have higher reproductive success than unflanged males due to flanged male dominance and female preference and (2) a single male would not monopolize paternity due to the temporal and spatial distribution of fecund females. We used fecal samples collected in Gunung Palung National Park from 2008-2019 to genotype orangutans (13 offspring born 2002-2015, their 10 mothers, and 19 candidate sires) using 12 microsatellites. MICROCHECKER 2.2.3 and CERVUS 3.0 were used to confirm the suitability of the microsatellite panel, fidelity of individual identities, and genetic maternity. Paternity analysis was performed with both CERVUS 3.0 and COLONY 2.0.6.7. We were able to identify paternity for six offspring. Four flanged males sired five offspring, and one sire’s morph was unknown at the time of conception. We found that flanged males have higher reproductive success and that females are not monopolizable in this completely wild setting. We discuss the implications of all published orangutan paternity results for understanding bimaturism in orangutans. 

We present a method for estimating the amount of matter in large-scale (approximately 50 Mpc) filaments using the surrounding velocity infall pattern, based on 242 filaments in the Millennium simulation. We identify filaments using a minimal spanning tree to link large groups and clusters, and find the axis of each filament using a weighted principle component analysis. We improve our previous determination of a typical infall velocity profile by rescaling the profile for each filament by the distance where the infall speed reaches a maximum. We use the resulting average profile to determine a two-parameter piecewise function that can be used to estimate the maximum infall speed and location for individual filaments. Finally, we present the correlation between the maximum infall speed and the mass of the filament. These results will be used as part of the Arecibo Pisces-Perseus Supercluster Survey (APPSS), a project to map the infall pattern around the Pisces-Perseus Supercluster filament. This work is supported by NSF grant AST-1637339. 

In preparation for comparison with the Arecibo Pisces-Perseus Supercluster Survey (APPSS), we present the theoretically expected density and velocity profiles for large-scale (~ 50 Mpc) filaments from the Millennium simulation. We use an observationally-friendly method to identify filaments using the positions of large groups of galaxies, and average filaments together to find the typical structure of a filament in terms of cylindrical density profile and velocity infall profile. Both profiles can be fit by simple functions, but show a large scatter across the population of filaments. We are in the process of categorizing filaments to facilitate comparison with observations of specific filaments, like the Pisces-Perseus Supercluster filament. This work has been supported by NSF grant AST-1637339. 

Abstract A diverse set of white matter connections supports seamless transitions between cognitive states. However, it remains unclear how these connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we analyze the brain’s trajectories across a set of single time point activity patterns from functional magnetic resonance imaging data acquired during the resting state and an n-back working memory task. We find that specific temporal sequences of brain activity are modulated by cognitive load, associated with age, and related to task performance. Using diffusion-weighted imaging acquired from the same subjects, we apply tools from network control theory to show that linear spread of activity along white matter connections constrains the probabilities of these sequences at rest, while stimulus-driven visual inputs explain the sequences observed during the n-back task. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics. 

Adolescents are known for taking risks, from driving too fast to experimenting with drugs and alcohol. Such behaviors tend to decrease as individuals move into adulthood. Most people in their mid-twenties have greater self-control than they did as teenagers. They are also often better at planning, sustaining attention, and inhibiting impulsive behaviors. These skills, which are known as executive functions, develop over the course of adolescence. Executive functions rely upon a series of brain regions distributed across the frontal lobe and the lobe that sits just behind it, the parietal lobe. Fiber tracts connect these regions to form a fronto-parietal network. These fiber tracts are also referred to as white matter due to the whitish fatty material that surrounds and insulates them. Cui et al. now show that changes in white matter networks have implications for teen behavior. Almost 950 healthy young people aged between 8 and 23 years underwent a type of brain scan called diffusion-weighted imaging that visualizes white matter. The scans revealed that white matter networks in the frontal and parietal lobes mature over adolescence. This makes it easier for individuals to activate their fronto-parietal networks by decreasing the amount of energy required. Cui et al. show that a computer model can predict the maturity of a person's brain based on the energy needed to activate their fronto-parietal networks. These changes help explain why executive functions improve during adolescence. This in turn explains why behaviors such as risk-taking tend to decrease with age. That said, adults with various psychiatric disorders, such as ADHD and psychosis, often show impaired executive functions. In the future, it may be possible to reduce these impairments by applying magnetic fields to the scalp to reduce the activity of specific brain regions. The techniques used in the current study could help reveal which brain regions to target with this approach. 

Executive function is a quintessential human capacity that emerges late in development and displays different developmental trends in males and females. Sex differences in executive function in youth have been linked to vulnerability to psychopathology as well as to behaviors that impinge on health, wellbeing, and longevity. Yet, the neurobiological basis of these differences is not well understood, in part due to the spatiotemporal complexity inherent in patterns of brain network maturation supporting executive function. Here we test the hypothesis that sex differences in impulsivity in youth stem from sex differences in the controllability of structural brain networks as they rewire over development. Combining methods from network neuroscience and network control theory, we characterize the network control properties of structural brain networks estimated from diffusion imaging data acquired in males and females in a sample of 879 youth aged 8–22 years. We summarize the control properties of these networks by estimating average and modal controllability, two statistics that probe the ease with which brain areas can drive the network towards easy versus difficult-to-reach states. We find that females have higher modal controllability in frontal, parietal, and subcortical regions while males have higher average controllability in frontal and subcortical regions. Furthermore, controllability profiles in males are negatively related to the false positive rate on a continuous performance task, a common measure of impulsivity. Finally, we find associations between average controllability and individual differences in activation during an n-back working memory task. Taken together, our findings support the notion that sex differences in the controllability of structural brain networks can partially explain sex differences in executive function. Controllability of structural brain networks also predicts features of task-relevant activation, suggesting the potential for controllability to represent context- specific constraints on network state more generally.