Back

Brain Network Connectivity in Adolescents At Risk for Alcohol Use Problems

Baker, S. T. E.; Yucel, M.; Fornito, A.; Zalesky, A.; Whittle, S.; Allen, N. B.; Lubman, D. I.

2020-10-27 psychiatry and clinical psychology
10.1101/2020.10.22.20217976 medRxiv
Show abstract

Imaging studies of young people with a family history of alcohol use disorder (AUD) have found structural and/or functional differences within and between anatomically distributed and functionally specialised systems throughout the brain. Differences in brain connectivity among adolescents with a family history of AUD may account for the increased risk of later alcohol use problems; however, to date, no prospective studies have directly examined this hypothesis across the entire connectome in a regionally unbiased way. Our analysis included 52 adolescents (Mage = 16.5 years {+/-} 0.6 SD) assessed with diffusion-weighted magnetic resonance imaging, of whom 20 had a family history of AUD and 32 did not. All participants were followed-up 2.3 years later and completed a questionnaire measuring past year alcohol use and alcohol-related harms. Subject-specific connectomic maps of structural connectivity were constructed using two parcellation schemes (82-node anatomical and 530-node random) and five measures of connectivity weight (streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity), and a connectome-wide network-based statistic analysis was used to compare group differences at each and every connection between adolescents with and without a family history of AUD. Baseline connectivity measures did not differentiate these groups, and we did not find an association between baseline connectivity measures and alcohol outcomes at follow-up. These findings suggest that atypical inter-regional structural connectivity may not contribute to the risk of developing alcohol use problems in this particular age group, or during this particular period of development.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.