Gender bias in academia: A lifetime problem that needs solutions

Summary:

Despite increased awareness of the lack of gender equity in academia and a growing number of initiatives to address issues of diversity, change is slow, and inequalities remain. A major source of inequity is gender bias, which has a substantial negative impact on the careers, work-life balance, and mental health of underrepresented groups in science. Here, we argue that gender bias is not a single problem but manifests as a collection of distinct issues that impact researchers’ lives. We disentangle these facets and propose concrete solutions that can be adopted by individuals, academic institutions, and society.

Authors:

• Anaïs Llorens

• Athina Tzovara

• Ludovic Bellier

• Ilina Bhaya-Grossman

• Aurélie Bidet-Caulet

• William K Chang

• Zachariah R Cross

• Rosa Dominguez-Faus

• Adeen Flinker

• Yvonne Fonken

• Mark A Gorenstein

• Chris Holdgraf

• Colin W Hoy

• Maria V Ivanova

• Richard T Jimenez

• Soyeon Jun

• Julia WY Kam

• Celeste Kidd

• Enitan Marcelle

• Deborah Marciano

• Stephanie Martin

• Nicholas E Myers

• Karita Ojala

• Anat Perry

• Pedro Pinheiro-Chagas

• Stephanie K Riès

• Ignacio Saez

• Ivan Skelin

• Katarina Slama

• Brooke Staveland

• Danielle S Bassett

• Elizabeth A Buffalo

• Adrienne L Fairhall

• Nancy J Kopell

• Laura J Kray

• Jack J Lin

• Anna C Nobre

• Dylan Riley

• Anne-Kristin Solbakk

• Joni D Wallis

• Xiao-Jing Wang

• Shlomit Yuval-Greenberg

• Sabine Kastner

• Robert T Knight

• Nina F Dronkers

Date: 2021

DOI: https://doi.org/10.1016/j.neuron.2021.06.002

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Parameterizing neural power spectra into periodic and aperiodic components

Abstract:

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis.

Authors:

• Thomas Donoghue

• Matar Haller

• Erik J Peterson

• Paroma Varma

• Priyadarshini Sebastian

• Richard Gao

• Torben Noto

• Antonio H Lara

• Joni D Wallis

• Robert T Knight

• Avgusta Shestyuk

• Bradley Voytek

Date: 2020

DOI: https://doi.org/10.1038/s41593-020-00744-x

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