Systematic Reviews

[In progress] Natural Language Processing (NLP) for Systematic Reviews

Comparison of BERT models (Bidirectional Encoder Representations from Transformers) used to screen literature for meta-analysis in the field of neuroscience.

Contribution: Co-author

Collaborators: Henk-Jan Boele, Peter Boele, Samuel Wang

[In progress] Racial bias in judicial sentencing: a meta-analysis

Contribution: Co-author

Collaborators: Joseph Avery, Jihyun Lee, Joel Cooper

[In progress] Princeton Brain Development Project + Meta-analysis

"The Princeton Brain Development project aims to model the number of brain connections during our life. Connections between brain cells are called synapses. Babies produce thousands of synapses per second. This leads to a peak in synapse density early in childhood. After this peak, the unused synapses are slowly eliminated again, which leads to a slow decline in synapse density that reaches a stable plateau during adulthood. Understanding the shape of this curve is important to understand diseases like autism and schizophrenia. The Princeton Brain Development project collects data from original research papers. We do not limit ourselves to data obtained from human brains, since most pre-clinical work is done in non-human species."


Henk-Jan Boele, M.D., Ph.D., Princeton Neuroscience Institute

Samuel Wang, Professor of Molecular Biology and Princeton Neuroscience Institute



an infant wears a fNIRS head cap while sitting in an adult's lap.

[Preprint] Attrition Rate in Infant fNIRS Research: A Meta-Analysis


Preprint: Baek, S., Marques, S., Casey, K., Testerman, M., McGill, F., & Emberson, L. (2021). Attrition Rate in Infant fNIRS Research: A Meta-Analysis. In bioRxiv (p. 2021.06.15.448526).

Abstract: Understanding the trends and predictors of attrition rate, or the proportion of collected data that is excluded from the final analyses, is important for accurate research planning, assessing data integrity, and ensuring generalizability. In this pre-registered meta-analysis, we reviewed 182 publications in infant (0-24 months) functional near-infrared spectroscopy (fNIRS) research published from 1998 to April 9, 2020 and investigated the trends and predictors of attrition. The average attrition rate was 34.23% among 272 experiments across all 182 publications. Among a subset of 136 experiments which reported the specific reasons of subject exclusion, 21.50% of the attrition were infant-driven while 14.21% were signal-driven. Subject characteristics (e.g., age) and study design (e.g., fNIRS cap configuration, block/trial design, and stimulus type) predicted the total and subject-driven attrition rates, suggesting that modifying the recruitment pool or the study design can meaningfully reduce the attrition rate in infant fNIRS research. Based on the findings, we established guidelines on reporting the attrition rate for scientific transparency and made recommendations to minimize the attrition rates. We also launched an attrition rate calculator (LINK) to aid with research planning. This research can facilitate developmental cognitive neuroscientists in their quest toward increasingly rigorous and representative research.

Text: NIRO Systematic Review

[Preprint] NIRO: Non-interventional, Reproducible, and Open Systematic Reviews

A large, open collaborative project to create guidelines for non-interventional systematic reviews.

Tool v0.1


Topor, M., Pickering, J. S., Barbosa Mendes, A., Bishop, D. V. M., Büttner, F. C., Henderson, E. L., … Westwood, S. J. (2020, December 14). An integrative framework for planning and conducting Non-Interventional, Reproducible, and Open Systematic Reviews (NIRO-SR).


Co-author: NIRO Tool

Co-author: Search strategy supplementary publication


Web app: NIRO


Example protocols:

Lam, A. (2020, August 26). Folk conceptions of free will: A narrative systematic review of psychological research.