Danielle S. Bassett

University of Pennsylvania · Rehabilitation Medicine

Active 1960–2024

h-index122

Citations73.3k

Papers1.0k430 last 5y

Funding$14.8M1 active

Faculty page Lab page Website

See your match with Danielle S. Bassett — sign in to PhdFit.Sign in

About

Danielle S. Bassett is a researcher affiliated with the Complex Systems Lab at the University of Pennsylvania. Her work focuses on complex systems, network science, systems neuroscience, cognitive and clinical neuroscience, neuroimaging, psychology, computational biology, computer science, and social networks. She has contributed to the understanding of the relations among things within complex systems, emphasizing the importance of relations over individual entities. Bassett has authored a book titled 'Curious Minds,' published by MIT Press, and has been featured in numerous interviews and media outlets discussing her research and insights into complex systems and curiosity. She is actively involved in fostering diversity within her lab, welcoming applications from talented undergraduate, graduate, and postdoctoral scholars from diverse academic backgrounds. Her research aims to explore the relations among components of complex systems, contributing to the broader understanding of how these systems function and evolve. Bassett's work is characterized by an interdisciplinary approach, integrating principles from physics, mathematics, engineering, neuroscience, and social sciences to advance the field of complex systems science.

Research topics

Computer Science
Artificial Intelligence
Psychology
Sociology
Political Science
Data Mining
Neuroscience
Law
Database
Cognitive psychology
Medicine
Gender studies
Geography
Social Science
Computer vision
Mathematics
Cognitive science
Social psychology
Physics
Public relations
Developmental psychology
Library science
Business
Media studies

Selected publications

Environmental influences on the pace of brain development
Nature reviews. Neuroscience · 2021 · 494 citations
- Computer Science
- Psychology
- Developmental psychology
Childhood socio-economic status (SES), a measure of the availability of material and social resources, is one of the strongest predictors of lifelong well-being. Here we review evidence that experiences associated with childhood SES affect not only the outcome but also the pace of brain development. We argue that higher childhood SES is associated with protracted structural brain development and a prolonged trajectory of functional network segregation, ultimately leading to more efficient cortical networks in adulthood. We hypothesize that greater exposure to chronic stress accelerates brain maturation, whereas greater access to novel positive experiences decelerates maturation. We discuss the impact of variation in the pace of brain development on plasticity and learning. We provide a generative theoretical framework to catalyse future basic science and translational research on environmental influences on brain development.
DOI
Gender bias in academia: A lifetime problem that needs solutions
Neuron · 2021 · 337 citations
- Political Science
- Sociology
- Psychology
DOI
Gendered citation practices in the field of communication
Annals of the International Communication Association · 2021 · 137 citations
- Sociology
- Political Science
- Sociology
In disciplines outside of communication, papers with women as first and last (i.e., senior) authors attract fewer citations than papers with men in those positions. Using data from 14 communication journals from 1995 to 2018, we find that reference lists include more papers with men as first and last author, and fewer papers with women as first and last author, than would be expected if gender were unrelated to referencing. This imbalance is driven largely by the citation practices of men and is slowly decreasing over time. The structure of men's co-authorship networks partly accounts for the observed over-citation of men by other men. We discuss ways researchers might approach gendered citations in their work.
DOI
QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data
Nature Methods · 2021 · 315 citations
- Computer Science
- Computer Science
- Data Mining
DOI
Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology
Neuron · 2021 · 733 citations
- Neuroscience
- Psychology
- Biology
DOI
Racial and ethnic imbalance in neuroscience reference lists and intersections with gender
bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 203 citations
Senior authorCorresponding
- Political Science
- Sociology
- Psychology
Discrimination against racial and ethnic minority groups exists in the academy, and the associated biases impact hiring and promotion, publication rates, grant funding, and awards. Precisely how racial and ethnic bias impacts the manner in which the scientific community engages with the ideas of academics in minority groups has yet to be fully elucidated. Citations are a marker of such community engagement, as well as a currency used to attain career milestones. Here we assess the extent and drivers of racial and ethnic imbalance in the reference lists of papers published in five top neuroscience journals over the last 25 years. We find that reference lists tend to include more papers with a White person as first and last author than would be expected if race and ethnicity were unrelated to referencing. We show that this imbalance is driven largely by the citation practices of White authors, and is increasing over time even as the field diversifies. To further explain our findings, we examine co-authorship networks and find that while the network has become markedly more integrated in general, the current degree of segregation by race/ethnicity is greater now than it has been in the past. Citing further from oneself on the network is associated with greater balance, but White authors’ preferential citation of White authors remains even at high levels of network exploration. We also quantify the effects of intersecting identities, determining the relative costs of gender and race/ethnicity, and their combination in women of color. Our findings represent a call to scientists and journal editors of all disciplines to consider the ethics of citation practices, and actions to be taken in support of an equitable future.
DOI
Leveraging multi-shell diffusion for studies of brain development in youth and young adulthood
Developmental Cognitive Neuroscience · 2020 · 93 citations
- Artificial Intelligence
- Computer Science
- Psychology
Diffusion weighted imaging (DWI) has advanced our understanding of brain microstructure evolution over development. Recently, the use of multi-shell diffusion imaging sequences has coincided with advances in modeling the diffusion signal, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). However, the relative utility of recently-developed diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the vulnerability of metrics derived from contemporary models to in-scanner motion has not been described. Accordingly, in a sample of 120 youth and young adults (ages 12-30) we evaluated metrics derived from diffusion tensor imaging (DTI), NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales. Specifically, we examined mean white matter values, white matter tracts, white matter voxels, and connections in structural brain networks. Our results revealed that multi-shell diffusion imaging data can be leveraged to robustly characterize neurodevelopment, and demonstrate stronger age effects than equivalent single-shell data. Additionally, MAPL-derived metrics were less sensitive to the confounding effects of head motion. Our findings suggest that multi-shell imaging data and contemporary modeling techniques confer important advantages for studies of neurodevelopment.
Publisher DOI
Functional brain network architecture supporting the learning of social networks in humans
NeuroImage · 2020 · 44 citations
Senior authorCorresponding
- Computer Science
- Psychology
- Cognitive psychology
Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. We found that participants learned the community structure of the networks, as evidenced by a slower reaction time when a trial moved between communities than when a trial moved within a community. Learning the community structure of social networks was also characterized by significantly greater functional connectivity of the hippocampus and temporoparietal junction when transitioning between communities than when transitioning within a community. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions for social networks than for non-social networks. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.
DOI
Functional brain network reconfiguration during learning in a dynamic environment
Nature Communications · 2020 · 62 citations
- Computer Science
- Artificial Intelligence
- Computer Science
When learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.
DOI
QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI
bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 25 citations
- Computer Science
- Computer Science
- Artificial Intelligence
ABSTRACT Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as “glass-box” methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.
DOI