Add DIVA preprint (#353)

grab_articles.py

@@ -139,7 +139,7 @@
     data=rows,
 )
 df = df.sort_values(by=["pmid"])
-df.to_csv("articles.tsv", sep="\t", line_terminator="\n", index=False)
+df.to_csv("articles.tsv", sep="\t", lineterminator="\n", index=False)
 df = df.fillna("")
 
 # Grab our markdown file template

papers/_posts/2024-04-16-bottenhorn-salo-diva.md

@@ -0,0 +1,45 @@
+---
+layout: paper
+title: "Dense Investigation of Variability in Affect (DIVA): A Neuroimaging Study of Premenopausal Female Participants"
+nickname: 2024-04-16-bottenhorn-salo-diva
+authors: "Bottenhorn KL, Salo T, Peraza JA, Riedel MC, Flannery JS, Kimbler A, Toll A, Suarez D, Cruz FM, Zagales I, Caldes N, Dolan O, Zagales R, Sutherland MT, Laird RW, Laird AR"
+year: "2024"
+journal: bioRxiv
+volume:
+issue:
+pages:
+is_published: false
+image: /assets/images/papers/biorxiv.png
+projects: [diva]
+tags: [preprint]
+
+# Text
+fulltext:
+pdf:
+pdflink:
+pmcid:
+preprint: https://www.biorxiv.org/content/10.1101/2024.04.16.589598
+supplement:
+
+# Links
+doi: "10.1101/2024.04.16.589598"
+pmid:
+
+# Data and code
+github: ["https://github.com/NBCLab/diva-project", "https://github.com/NBCLab/arithmetic-task", "https://github.com/NBCLab/pyfLoc", "https://github.com/NBCLab/localizer-task", "https://github.com/NBCLab/film-viewing-task", "https://github.com/NBCLab/arithmetic-task", "https://github.com/NBCLab/sorpf-task", "https://github.com/NBCLab/eirt-task", "https://github.com/NBCLab/probabilistic-selection-task"]
+neurovault:
+openneuro: ["ds002278"]
+figshare:
+figshare_names:
+osf:
+---
+{% include JB/setup %}
+
+# Abstract
+
+The rise of large neuroimaging datasets and multi-dataset mega-analyses brings the power to study interindividual differences in brain structure and function on a heretofore unseen scale.
+However, unknown and poorly characterized intra-individual variability continues to undermine the detection of robust brain-behavior associations and, ultimately, our understanding of the brain on the whole.
+Women's and reproductive health underlie variability in more than half of the population, but have long been overlooked in the study of both inter- and intra-individual differences in the brain.
+To this end, the Dense Investigation of Variability in Affect (DIVA) Study was designed to study intra-individual variability in the brain and behavior across the menstrual cycle in a small cohort of premenopausal female participants.
+The DIVA Study acquired weekly actigraphy, self-report, biospecimen, and both functional and structural magnetic resonance imaging data with concurrent peripheral physiological recordings.
+These data facilitate the study of several common sources of variability in the brain and behavior: the menstrual cycle and ovarian hormones, sleep, stress, exercise, and exogenous sources of hemodynamic variability.