Quantifying the role of neurons for behavior is a mediation question

Many systems neuroscientists want to understand neurons in terms of mediation; we want to understand how neurons are involved in the causal chain from stimulus to behavior. Unfortunately, most tools are inappropriate for that while our language takes mediation for granted. Here we discuss the contrast between our conceptual drive towards mediation and the difficulty of obtaining meaningful evidence.Comment: 4 pages, 2 figure

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

DNA methylation biomarkers prospectively predict both antenatal and postpartum depression

We sought to replicate and expand upon previous work demonstrating antenatal TTC9B and HP1BP3 gene DNA methylation is prospectively predictive of postpartum depression (PPD) with ~80% accuracy. In a preterm birth study from Emory, Illumina MethylEPIC microarray derived 1st but not 3rd trimester biomarker models predicted 3rd trimester Edinburgh Postnatal Depression Scale (EPDS) scores ≥ 13 with an AUC=0.8 (95% CI: 0.63-0.8). Bisulfite pyrosequencing derived biomarker methylation was generated using bisulfite pyrosequencing across all trimesters in a pregnancy cohort at UC Irvine and in 3rd trimester from an independent Johns Hopkins pregnancy cohort. A support vector machine model incorporating 3rd trimester EPDS scores, TTC9B, and HP1BP3 methylation status predicted 4 week to 6 week postpartum EPDS ≥ 13 from 3rd trimester blood in the UC Irvine cohort (AUC=0.78, 95% CI: 0.64-0.78) and from the Johns Hopkins cohort (AUC=0.84, 95% CI: 0.72-0.97), both independent of previous psychiatric diagnosis. Technical replicate predictions in a subset of the Johns Hopkins cohort exhibited strong cross experiment correlation. This study confirms the PPD prediction model has the potential to be developed into a clinical tool enabling the identification of pregnant women at future risk of PPD who may benefit from clinical intervention