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Purpose: Declining energy and increasing fatigue, common in older age, predict neurodegenerative conditions, but their neural substrates are not known. We examined brain resting state connectivity in relation to declining self-reported energy levels (SEL) and occurrence of fatigue over time., Methods: We examined resting-state functional MRI in 272 community dwelling older adults participating in the Health Aging and Body Composition Study (mean age 83 years; 57.4 % female; 40.8 % Black) with measures of fatigue and SEL collected at regular intervals over the prior ten years. Functional connectivity (FC) between cortex and striatum was examined separately for sensorimotor, executive, and limbic functional subregions. Logistic regression tested the association of FC in each network with prior fatigue state (reporting fatigue at least once or never reporting fatigue), and with SEL decline (divided into stable or declining SEL groups) and adjusted for demographic, physical function, mood, cognition, and comorbidities., Results: Higher cortico-striatal FC in the right limbic network was associated with lower odds of reporting fatigue (better) at least once during the study period (adjusted odds ratio [95 % confidence interval], p-value: (0.747 [0.582, 0.955], 0.020), independent of SEL. Higher cortico-striatal FC in the right executive network was associated with higher odds of declining SEL (worse) during the study period (adjusted odds ratio [95 % confidence interval], p-value: (1.31 [1.01, 1.69], 0.041), independent of fatigue. Associations with other networks were not significant., Conclusions: In this cohort of older adults, the cortico-striatal functional connectivity of declining SEL appears distinct from that underlying fatigue. Studies to further assess the neural correlates of energy and fatigue, and their independent contribution to neurodegenerative conditions are warranted., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: White matter hyperintensities (WMH) may promote clinical Alzheimer's disease (AD) disparities between Black American (BA) and non-Hispanic White (nHW) populations. Using a novel measurement, unhealthy white matter connectivity (UWMC), we interrogated racialized group differences in associations between WMH in AD pathology-affected regions and cognition., Methods: UWMC is the proportion of white matter fibers that pass through WMH for every pair of brain regions. Individual regression models tested associations of UWMC in beta-amyloid (Aβ) or tau pathology-affected regions with cognition overall, stratified by racialized group, and with a racialized group interaction., Results: In 201 older adults ranging from cognitively unimpaired to AD, BA participants exhibited greater UWMC and worse cognition than nHW participants. UWMC was negatively associated with cognition in 17 and 5 Aβ- and tau-affected regions, respectively. Racialization did not modify these relationships., Discussion: Differential UWMC burden, not differential UWMC-and-cognition associations, may drive clinical AD disparities between racialized groups., Highlights: Unhealthy white matter connectivity (UWMC) in Alzheimer's disease (AD) pathology-affected brain regions is associated with cognition. Relationships between UWMC and cognition are similar between Black American (BA) and non-Hispanic White (nHW) individuals. More UWMC may partially drive higher clinical AD burden in BA versus nHW populations. UWMC risk factors, particularly social and environmental, should be identified., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: Older adults reporting higher energy levels have better physical function. It is not known if these associations persist among older adults reporting fatigue or if higher energy is associated with cognitive function. We examined longitudinal associations between self-reported energy, gait speed, and cognition, stratified by fatigue, in 2 613 participants (aged 74.6 ± 2.87 years) in the Health, Aging and Body Composition Study., Methods: Self-reported energy (0-10, dichotomized at median) and fatigue (present/absent) were measured at baseline. Usual and rapid-paced gait speed (m/s), modified Mini-Mental State Examination (3MS), and Digit Symbol Substitution Test (DSST) were measured at baseline and annually over 8 years. Linear mixed effect models compared changes in gait speed, 3MS, and DSST between higher and lower energy groups within fatigue strata., Results: At baseline, 724 participants (27%) were fatigued; 240 (33%) coreported higher energy (9% of total). The remaining 1 889 participants were fatigue-free (73%); 1 221 (65%) coreported higher energy (47% of total). Those with fatigue and higher energy had average rapid gait declines of 0.007 m/s per year (p = .04) after adjustment for demographics, comorbidities, depressive symptoms, and exercise. DSST declines were found among only fatigue-free participants (β = 0.17, p = .01). No statistically significant associations with energy were found for fatigue-free participants, or for usual gait or 3MS., Conclusions: Asking about older adults' energy levels as well as fatigue may identify a subgroup of older adults protected against physical and cognitive decline, even among those with fatigue., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Purpose: Mild Parkinsonian signs (MPS) are common in older adults. We hypothesized that MPS are associated with lower functional connectivity (FC) in dopamine-dependent cortico-striatal networks, and these associations vary with white matter hyperintensity (WMH), a risk factor for MPS., Methods: We examined resting-state functional MRI in 266 participants (mean age 83; 57% female; 41% African American) with data on MPS (Unified Parkinson's Disease Rating Scale), demographics, cognition, muscle-skeletal, and cardiometabolic health. FC between cortex and striatum was examined separately for sensorimotor, executive, and limbic functional subregions. Logistic regression tested the association of FC in each network with MPS, adjusted for covariates. Interactions of FC by WMH were tested; and analyses were repeated stratified by WMH above/below the median., Results: Compared to those without MPS, those with MPS had lower cortico-striatal FC in the left executive network (adjusted odds ratio [95% confidence interval], p-value: 0.188 [0.043, 0.824], .027). The interaction with WMH was p = .064; left executive FC was inversely associated with MPS for high WMH (0.077 [0.010, 0.599], .014) but not low WMH participants (1.245 [0.128, 12.132], .850)., Conclusions: MPS appear related to lower executive network FC, robust to adjustment for other risk factors, and stronger for those with higher burden of WMH. Future longitudinal studies should examine the interplay between cerebral small vessel disease and connectivity influencing MPS., (© 2021 American Society of Neuroimaging.)

Olfaction informs animal navigation for foraging, social interaction, and threat evasion. However, turbulent flow on the spatial scales of most animal navigation leads to intermittent odor information and presents a challenge to simple gradient-ascent navigation. Here we present two strategies for iterative gradient estimation and navigation via olfactory cues in 2D space: tropotaxis, spatial concentration comparison (i.e., instantaneous comparison between lateral olfactory sensors on a navigating animal) and klinotaxis, spatiotemporal concentration comparison (i.e., comparison between two subsequent concentration samples as the animal moves through space). We then construct a hybrid model that uses klinotaxis but utilizes tropotactic information to guide its spatial sampling strategy. We find that for certain body geometries in which bilateral sensors are closely-spaced (e.g., mammalian nares), klinotaxis outperforms tropotaxis; for widely-spaced sensors (e.g., arthropod antennae), tropotaxis outperforms klinotaxis. We find that both navigation strategies perform well on smooth odor gradients and are robust against noisy gradients represented by stochastic odor models and real turbulent flow data. In some parameter regimes, the hybrid model outperforms klinotaxis alone, but not tropotaxis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Mathematical modeling of developmental signaling networks has played an increasingly important role in the identification of regulatory mechanisms by providing a sandbox for hypothesis testing and experiment design. Whether these models consist of an equation with a few parameters or dozens of equations with hundreds of parameters, a prerequisite to model-based discovery is to bring simulated behavior into agreement with observed data via parameter estimation. These parameters provide insight into the system (e.g., enzymatic rate constants describe enzyme properties). Depending on the nature of the model fit desired - from qualitative (relative spatial positions of phosphorylation) to quantitative (exact agreement of spatial position and concentration of gene products) - different measures of data-model mismatch are used to estimate different parameter values, which contain different levels of usable information and/or uncertainty. To facilitate the adoption of modeling as a tool for discovery alongside other tools such as genetics, immunostaining, and biochemistry, careful consideration needs to be given to how well a model fits the available data, what the optimized parameter values mean in a biological context, and how the uncertainty in model parameters and predictions plays into experiment design. The core discussion herein pertains to the quantification of model-to-data agreement, which constitutes the first measure of a model's performance and future utility to the problem at hand. Integration of this experimental data and the appropriate choice of objective measures of data-model agreement will continue to drive modeling forward as a tool that contributes to experimental discovery. The Drosophila melanogaster gap gene system, in which model parameters are optimized against in situ immunofluorescence intensities, demonstrates the importance of error quantification, which is applicable to a wide array of developmental modeling studies., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The axial bodyplan of Drosophila melanogaster is determined during a process called morphogenesis. Shortly after fertilization, maternal bicoid mRNA is translated into Bicoid (Bcd). This protein establishes a spatially graded morphogen distribution along the anterior-posterior (AP) axis of the embryo. Bcd initiates AP axis determination by triggering expression of gap genes that subsequently regulate each other's expression to form a precisely controlled spatial distribution of gene products. Reaction-diffusion models of gap gene expression on a 1D domain have previously been used to infer complex genetic regulatory network (GRN) interactions by optimizing model parameters with respect to 1D gap gene expression data. Here we construct a finite element reaction-diffusion model with a realistic 3D geometry fit to full 3D gap gene expression data. Though gap gene products exhibit dorsal-ventral asymmetries, we discover that previously inferred gap GRNs yield qualitatively correct AP distributions on the 3D domain only when DV-symmetric initial conditions are employed. Model patterning loses qualitative agreement with experimental data when we incorporate a realistic DV-asymmetric distribution of Bcd. Further, we find that geometry alone is insufficient to account for DV-asymmetries in the final gap gene distribution. Additional GRN optimization confirms that the 3D model remains sensitive to GRN parameter perturbations. Finally, we find that incorporation of 3D data in simulation and optimization does not constrain the search space or improve optimization results.