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Abstract We analyze a magnetotail reconnection onset event on 3 July 2017 that was observed under otherwise quiescent magnetospheric conditions by a fortuitous conjunction of six space and ground‐based observatories. The study investigates the large‐scale coupling of the solar wind–magnetosphere system that precipitated the onset of the magnetotail reconnection, focusing on the processes that thinned and stretched the cross‐tail current layer in the absence of significant flux loading during a 2‐hr‐long preconditioning phase. It is demonstrated with data in the (a) upstream solar wind, (b) at the low‐latitude magnetopause, (c) in the high‐latitude polar cap, and (d) in the magnetotail that the typical picture of solar wind‐driven current sheet thinning via flux loading does not appear relevant for this particular event. We find that the current sheet thinning was, instead, initiated by a transient solar wind pressure pulse and that the current sheet thinning continued even as the magnetotail and solar wind pressures decreased. We suggest that field line curvature‐induced scattering (observed by magnetospheric multiscale) and precipitation (observed by Defense Meteorological Satellite Program) of high‐energy thermal protons may have evacuated plasma sheet thermal energy, which may require a thinning of the plasma sheet to preserve pressure equilibrium with the solar wind. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.