ehaviors related to self-regulation, such as substance use disorders or antisocial behaviors, have far-reaching consequences for affected individuals, their families, communities and society at large 1,2 . Collectively, this group of correlated traits are classified as externalizing 3 . Twin studies have demonstrated that externalizing liability is highly heritable (~80%) 4,5 . To date, however, no large-scale molecular genetic studies have utilized the extensive degree of genetic overlap among externalizing traits to aid gene discovery, as most studies have focused on individual disorders 6 . For many high-cost, high-risk behaviors with an externalizing component-opioid use disorder and suicide attempts 7 being salient examples-there are limited genotyped cases available for gene discovery 8,9 .A complementary strategy to the single-disease approach is to study the shared genetic architecture across traits in multivariate analyses, which boosts statistical power by pooling data across

BACKGROUND: Aging-related cognitive decline is a primary risk factor for Alzheimer's disease and related dementias. More precise identification of the neurobiological bases of cognitive decline in aging populations may provide critical insights into the precursors of late-life dementias. METHODS: Using structural and diffusion brain magnetic resonance imaging data from the UK Biobank (n = 8185; age range, 45-78 years), we examined aging of regional gray matter volumes (nodes) and white matter structural connectivity (edges) within 9 well-characterized networks of interest in the human brain connectome. In the independent Lothian Birth Cohort 1936 (n = 534; all 73 years of age), we tested whether aging-sensitive connectome elements are enriched for key domains of cognitive function before and after controlling for early-life cognitive ability. RESULTS: In the UK Biobank, age differences in individual connectome elements corresponded closely with principal component loadings reflecting connectome-wide integrity (jr nodes j = .420; jr edges j = .583), suggesting that connectome aging occurs on broad dimensions of variation in brain architecture. In the Lothian Birth Cohort 1936, composite indices of node integrity were predictive of all domains of cognitive function, whereas composite indices of edge integrity were associated specifically with processing speed. Elements within the central executive network were disproportionately predictive of late-life cognitive function relative to the network's small size. Associations with processing speed and visuospatial ability remained after controlling for childhood cognitive ability. CONCLUSIONS: These results implicate global dimensions of variation in the human structural connectome in agingrelated cognitive decline. The central executive network may demarcate a constellation of elements that are centrally important to age-related cognitive impairments.

Whole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3,153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44-77 years), we constructed 85 × 85 node whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 ≤ |β| ≤ 0.406) than the consistency-based approach which retained only 30% of connections (0.140 ≤ |β| ≤ 0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC = 0.84) when matched at a plausible level of network sparsity (70%). Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean β ≤ |0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean β ≤ |0.219|, p < 0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.There has been a growing enthusiasm for work seeking to construct structural brain networks, or structural "connectomes" (Sporns et al., 2005), which map white matter connectivity between distal regions of the human brain. Structural connectomes can be estimated in vivo, at a macroscopic scale, using diffusion magnetic resonance imaging (dMRI...

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Behaviors and disorders related to self-regulation, such as substance use, antisocial conduct, and ADHD, are collectively referred to as externalizing and have a shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The identified loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results captures variation in a broad range of behavioral and medical outcomes that were not part of our genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions, and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental condition.