Running hydrodynamical simulations to produce mock data of large-scale structure and baryonic probes, such as the thermal Sunyaev-Zeldovich (tSZ) effect, at cosmological scales is computationally challenging. We propose to leverage the expressive power of deep generative models to find an effective description of the large-scale gas distribution and temperature. We train two deep generative models, a variational auto-encoder and a generative adversarial network, on pairs of matter density and pressure slices from the BAHAMAS hydrodynamical simulation. The trained models are able to successfully map matter density to the corresponding gas pressure. We then apply the trained models on 100 lines-of-sight from SLICS, a suite of N-body simulations optimised for weak lensing covariance estimation, to generate maps of the tSZ effect. The generated tSZ maps are found to be statistically consistent with those from BAHAMAS. We conclude by considering a specific observable, the angular cross-power spectrum between the weak lensing convergence and the tSZ effect and its variance, where we find excellent agreement between the predictions from BAHAMAS and SLICS, thus enabling the use of SLICS for tSZ covariance estimation.

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In 2021, I published an Exploratory Report entitled “A network psychometric approach to neurocognition in early Alzheimer’s disease” in Cortex. In this paper, I created and analysed network models of neuropsychological task scores in cognitively normal (CN), amnestic mild cognitive impairment (aMCI), and early Alzheimer’s disease (eAD) groups. I also generated four hypotheses:•Original Hypothesis 1: Episodic memory variables will be most central in confirmatory network models of aMCI.•Original Hypothesis 2: Category Fluency becomes increasingly central in network models of groups with more severe Alzheimer’s disease (AD).•Original Hypothesis 3: Memory-semantic-language and attention-speed-working memory clusters will emerge in confirmatory network models for aMCI, eAD, and AD groups. They will be more pronounced for groups with more severe AD.•Original Hypothesis 4: Semantic networks underlying Category Fluency performance support the acquisition of word list memoranda in aMCI and eAD. After publishing my original paper, I learned of differential variability, further statistical tests, and community detection algorithms relevant to these hypotheses. In this commentary, I report the results of supplementary analyses based on the same data and network models in my original paper. Accordingly, these supplementary analyses do not provide confirmatory evidence for my original hypotheses. Instead, they reflect an attempt ratify or refine my original hypotheses based on additional analytical techniques. Indeed, the results of these supplementary analyses prompt some minor but important revisions to my original hypotheses; I present revised hypotheses throughout this commentary. R code and output for all supplementary analyses is accessible online: https://osf.io/2a7uz/.

Published research reports inconsistent links between APOE-ε4 and cognition. We hypothesised that these links can be reliably captured by graph-theory analyses.Cognitive networks were calculated in 8,118 controls, 3,482 MCI patients and 4,573 Alzheimer’s dementia patients, recruited as part of the National Alzheimer’s Coordinating Center (NACC) initiative. Differences in nodal centrality were tested in two independent NACC sub-cohorts between ε4-carriers and non-carriers.A significant APOE-dependant effect emerged from the analysis of the Logical-Memory nodes in MCI patients in both sub-cohorts. While non-carriers showed equal centrality in immediate and delayed recall, the latter was significantly less central among carriers. These findings were replicated in the sub-groups of sole amnestic-MCI patients (n = 2,971). No effects were found in the other two diagnostic groups.APOE ε4 influences nodal properties of cognitive networks at the MCI stage. This highlights the importance of characterising the impact of risk factors on the entire cognitive network.

Deficits in working memory (WM) and processing speed (PS) are thought to undermine other cognitive functions in de novo Parkinson's disease (dnPD). However, these interrelationships are only partially understood. This study investigated whether there are stronger relationships between verbal WM and verbal episodic memory encoding and retrieval, whether verbal WM and PS have a greater influence on other aspects of cognitive functioning, and whether the overall strength of interrelationships among several cognitive functions differs in dnPD compared to health. Data for 198 healthy controls (HCs) and 293 dnPD patients were analysed. Participants completed a neuropsychological battery probing verbal WM, PS, verbal episodic memory, semantic memory, language and visuospatial functioning. Deficit analysis, network modelling and graph theory were combined to compare the groups. Results suggested that verbal WM performance, while slightly impaired, was more strongly associated with measures of verbal episodic memory encoding and retrieval, as well as other measured cognitive functions in the dnPD network model compared to the HC network model. PS task performance was impaired and more strongly associated with other neuropsychological task scores in the dnPD model. Associations among task scores were stronger overall in the dnPD model. Together, these results provide further evidence that WM and PS are important influences on the other aspects of cognitive functioning measured in this study in dnPD. Moreover, they provide novel evidence that verbal WM and PS might bear greater influence on the other measured cognitive functions and that these functions are more strongly intertwined in dnPD compared to health.

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Introduction: descriptions of the typical pattern of neurocognitive impairment in Alzheimer’s disease (AD) refer to relationships between neurocognitive domains as well as deficits within domains. However, the former of these relationships have not been statistically modelled. Accordingly, this study aimed to model the unique variance between neurocognitive variables in AD, amnestic mild cognitive impairment (aMCI), and cognitive normality (CN) using network analysis. Methods: Gaussian Graphical Models with Extended Bayesian Information Criterion model selection and graphical lasso regularisation were used to estimate network models of neurocognitive variables in AD (n = 229), aMCI (n = 397) and CN (n = 193) groups. The psychometric properties of the models were investigated using simulation and bootstrapping procedures. Exploratory analyses of network structure invariance across groups were conducted. Results: neurocognitive network models were estimated for each group and found to have good psychometric properties. Exploratory investigations suggested that network structure was not invariant across CN and aMCI (p = 0.03), CN and AD (p < 0.01), and aMCI and AD neurocognitive networks (p < 0.01).Conclusions: network analysis can be used to robustly model the relationships between neurocognitive variables in AD, aMCI and CN. Network structure was not invariant, suggesting that relationships between neurocognitive variables differ across groups along the AD spectrum. Points of convergence and contrast with latent-variable models are explored.