The extent and drivers of gender imbalance in neuroscience reference lists

Dworkin, Jordan D.; Linn, Kristin A.; Teich, Erin G.; Zurn, Perry; Shinohara, Russell T.; Bassett, Danielle S.

doi:10.1101/2020.01.03.894378

Cited by 92 publications

(142 citation statements)

References 58 publications

(72 reference statements)

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“…Excluding self-citations to the first and senior authors of our current paper, the references contain 56% (n=32) man-man, 12.5% (n=7) man-woman, 17.5% (n=10) woman-man, and 14% (n=8) woman-woman. Expected proportions reported in Dworkin et al (2020) 70 for 5 high-impact neuroscience journals are, respectively: 58.4%, 9.4%, 25.5%, and 6.7%. We look forward to future work that could help us to better understand how to support equitable practices in science.…”

Section: Citation Diversity Statementmentioning

confidence: 98%

“…Recent work in neuroscience and other fields has identified a bias in citation practices such that papers from women and other minorities are under-cited relative to the number of such papers in the field [70][71][72] . Here we sought to proactively consider choosing references that reflect the diversity of the field in thought, form of contribution, gender, race, geographic location, and other factors.…”

Section: Citation Diversity Statementmentioning

confidence: 99%

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Learning differentially reorganizes brain activity and connectivity

Bertolero

Adebimpe

Khambhati

et al. 2020

Preprint

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Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so, how that differential reorganization tracks stages of learning across distinct brain areas. Here, we address this gap in knowledge by measuring brain activity and functional connectivity in a longitudinal fMRI experiment in which healthy adult human participants learn the values of novel objects over the course of four days. An increasing similarity in activity or functional connectivity across subjects during learning reflects reorganization toward a common functional architecture. We assessed the presence of reorganization in activity and connectivity both during value learning and during the resting-state, allowing us to differentiate common elicited processes from intrinsic processes. We found a complex and dynamic reorganization of brain connectivity and activity-as a function of time, space, and performance-that occurs while subjects learn. Spatially localized brain activity reorganizes across the brain to a common functional architecture early in learning, and this reorganization tracks early learning performance. In contrast, spatially distributed connectivity reorganizes across the brain to a common functional architecture as training progresses, and this reorganization tracks later learning performance. Particularly good performance is associated with a sticky connectivity, that persists into the resting state. Broadly, our work uncovers distinct principles of reorganization in activity and connectivity at different phases of value learning, which inform the ongoing study of learning processes more generally.Although each human brain is unique, all brains share a common form. Functional connectivity, which measures the statistical similarity between the temporal activity of two brain regions, can vary appreciably across individuals, so much so that it serves as an individual's fingerprint 1 . Moreover, recent work has demonstrated that functional connectivity can predict task performance 2 , including the capacity for skill learning 3-5 . Similarly, a single cognitively demanding task can elicit quite different patterns and magnitudes of activity in different individuals 6 . Finally, individual differences in functional connectivity during rest are related to individual differences in brain activity during task performance 7 . Despite these notable instances of variance in brain activity and connectivity, there also exist several invariable subject-general patterns; macro-organizational principles of functional connectivity appear to be conserved throughout healthy normative populations 8,9 , and there are reliable group level task-induced organizations of brain activity 10 and functional connectivity 9 .Similarly, learning is a dynamic process with both similarities and differences across individuals; everyone learns differently, but much of the mechanics of learning ...

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Section: Citation Diversity Statementmentioning

confidence: 98%

Section: Citation Diversity Statementmentioning

confidence: 99%

Learning differentially reorganizes brain activity and connectivity

Bertolero

Adebimpe

Khambhati

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recent work in neuroscience and other fields has identified a bias in citation practices such that papers from women and other minorities are under-cited relative to the number of such papers in the field [80,81,82,83,84,85]. Here we sought to proactively consider choosing references that reflect the diversity of the field in thought, form of contribution, gender, and other factors.…”

Section: Citation Diversity Statementmentioning

confidence: 99%

“…Here we sought to proactively consider choosing references that reflect the diversity of the field in thought, form of contribution, gender, and other factors. We used automatic classification of gender based on the first names of the first and last authors [80], with possible combinations including male/male,…”

Section: Citation Diversity Statementmentioning

confidence: 99%

Efficient Coding in the Economics of Human Brain Connectomics

Zhou

Lynn

Cui

et al. 2020

Preprint

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In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, metabolic and information transfer efficiency across structural networks are not understood. In a large cohort of youth, we find metabolic costs associated with structural path strengths supporting information diffusion. Metabolism is balanced with the coupling of structures supporting diffusion and network modularity. To understand efficient network communication, we develop a theory specifying minimum rates of message diffusion that brain regions should transmit for an expected fidelity, and we test five predictions from the theory. We introduce compression efficiency, which quantifies differing trade-offs between lossy compression and communication fidelity in structural networks. Compression efficiency evolves with development, heightens when metabolic gradients guide diffusion, constrains network complexity, explains how rich-club hubs integrate information, and correlates with cortical areal scaling, myelination, and speed-accuracy trade-offs. Our findings elucidate how network structures and metabolic resources support efficient neural communication.

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“…Citation Diversity Statement Recent work in neuroscience and other fields has identified a bias in citation practices such that papers from women and other minorities are under-cited relative to the number of such papers in the field [52][53][54] . Here we sought to proactively consider choosing references that reflect the diversity of the field in thought, form of contribution, gender, race, geographic location, and other factors.…”

Section: Citationsmentioning

confidence: 99%

Deep Neural Networks Carve the Brain at its Joints

Bertolero

Bassett

2020

Preprint

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How an individual's unique brain connectivity determines that individual's cognition, behavior, and risk for pathology is a fundamental question in basic and clinical neuroscience. In seeking answers, many have turned to machine learning, with some noting the particular promise of deep neural networks in modelling complex non-linear functions. However, it is not clear that complex functions actually exist between brain connectivity and behavior, and thus if deep neural networks necessarily outperform simpler linear models, or if their results would be interpretable. Here we show that, across 52 subject measures of cognition and behavior, deep neural networks fit to each brain region's connectivity outperform linear regression, particularly for the brain's connector hubs-regions with diverse brain connectivity-whereas the two approaches perform similarly when fit to brain systems. Critically, averaging deep neural network predictions across brain regions results in the most accurate predictions, demonstrating the ability of deep neural networks to easily model the various functions that exists between regional brain connectivity and behavior, carving the brain at its joints. Finally, we shine light into the black box of deep neural networks using multislice network models. We determined that the relationship between connector hubs and behavior is best captured by modular deep neural networks. Our results demonstrate that both simple and complex relationships exist between brain connectivity and behavior, and that deep neural networks can fit both. Moreover, deep neural networks are particularly powerful when they are first fit to the various functions of a system independently and then combined. Finally, deep neural networks are interpretable when their architectures are structurally characterized using multislice network models. MainHuman cognitive neuroscience seeks to explain how the function of an individual's brain determines their behavior. The human brain's functional connectivity, as commonly measured by the pairwise Pearson's correlation coefficient between regional time series, has generated tremendous insight into the brain's network function [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] . A desire to leverage individual variability in these connections to predict cognition, behavior, and symptoms of mental illness has united neuroscientists, clinicians, and machine learning experts 20-32 . Yet, precisely which prediction algorithm will prove most efficacious is unknown. While deep neural networks display notable predictive power in other domains 33 , it is not clear whether their application to human brain connectivity is necessary or insightful 34 . Further, it is not known whether the mathematical functions that define the relationship between brain connectivity and a given outcome are sufficiently complex so as to require deep neural networks instead of simpler linear models 34,35 . Moreover, even should deep neural networks offer accurate predictions, their interpretability ha...

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The extent and drivers of gender imbalance in neuroscience reference lists

Cited by 92 publications

References 58 publications

Learning differentially reorganizes brain activity and connectivity

Learning differentially reorganizes brain activity and connectivity

Efficient Coding in the Economics of Human Brain Connectomics

Deep Neural Networks Carve the Brain at its Joints

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