About

Dr. Stephanie Noble is an Assistant Professor in the Department of Psychology, Department of Bioengineering, and Center for Cognitive and Brain Health at Northeastern University. Her research focuses on creating tools to facilitate more precise human neuroscience inference and prediction, operating at the intersection of data science, neuroscience, and open science. Her work addresses open questions in neuroimaging studies and introduces new methods to enhance their reliability and validity. She leads the NeuroPRISM Lab, which develops computational and statistical tools for precision neuroscience research, integrating measurement theory and precision medicine concepts. Dr. Noble holds a PhD from Yale University in the Interdepartmental Neuroscience Program and a BSE in Chemical & Biological Engineering from Princeton University. Beyond her academic pursuits, she co-founded goBlue Labs, an EEG startup, and has served as a consultant for Source Signal Imaging and Elite Warrior Identification.

Research topics

• Computer Science
• Machine Learning
• Political Science
• Sociology
• Psychology
• Cognitive psychology
• Neuroscience
• Telecommunications
• Business
• Marketing
• Psychiatry
• Biology
• Engineering
• Developmental psychology
• Clinical psychology

Selected publications

• Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers

  Nature Human Behaviour · 2026-04-15

  articleOpen access

  A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on neuroimaging data are increasingly used as tools for predicting behavioural phenotypes, enhancing precision medicine and improving generalizability compared with traditional MRI studies. However, the high dimensionality of brain connectivity data makes model interpretation challenging. Prevailing practices rely on selecting features and, implicitly, interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Despite its widespread use, how univariate feature selection balances the trade-off between simplification for optimizing modelling and oversimplification that misrepresents true neurobiology remains understudied. Here, using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that edges discarded by feature selection can achieve significant prediction accuracies while yielding different neurobiological interpretations. These results are observed across cognitive, developmental and psychiatric phenotypes, extend to both functional connectivity (functional MRI) and structural (diffusion tensor imaging) connectomes, and remain evident in external validation. They suggest that focusing on only the top features may simplify the neurobiological bases of brain-behaviour associations. Such interpretations present only the tip of the iceberg when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results reinforce that subtle brain-wide signals should not be ignored.

  PublisherDOI

• Contributors

  Elsevier eBooks · 2025-10-10

  book-chapter
  PublisherDOI

• Trends in self-citation rates in high-impact neurology, neuroscience, and psychiatry journals

  eLife · 2025-05-14

  articleOpen access

  Citation metrics influence academic reputation and career trajectories. Recent works have highlighted flaws in citation practices in the Neurosciences, such as the under-citation of women. However, self-citation rates—or how much authors cite themselves—have not yet been comprehensively investigated in the Neurosciences. This work characterizes self-citation rates in basic, translational, and clinical Neuroscience literature by collating 100,347 articles from 63 journals between the years 2000–2020. In analyzing over five million citations, we demonstrate four key findings: (1) increasing self-citation rates of Last Authors relative to First Authors, (2) lower self-citation rates in low- and middle-income countries, (3) gender differences in self-citation stemming from differences in the number of previously published papers, and (4) variations in self-citation rates by field. Our characterization of self-citation provides insight into citation practices that shape the perceived influence of authors in the Neurosciences, which in turn may impact what type of scientific research is done and who gets the opportunity to do it.

  PublisherDOI

• Connectome caricatures: large-amplitude co-activation patterns in resting-state fMRI hide sources of individual differences

  Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2025-09-16

  article

  Motivation: Task-like co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity. However, little research has characterized the remaining signal. Goal(s): We aimed to characterize the hidden resting-state signal that exists beyond the dominating co-activation patterns and assess its merit for studying individual differences. Approach: We used task-based fMRI data to construct a task-relevant co-activation pattern manifold. By projecting resting-state time series data orthogonally to this manifold, we created Caricatured connectomes. Results: Like caricatures, these connectomes emphasized individual differences while reducing between-individual similarity. They also represented individual differences in behavior, often to a greater degree than Standard connectomes. Impact: A distinct signal carrying information about individual differences exists beyond the dominating co-activations that drive resting-state functional connectivity. This signal may better characterize the brain's intrinsic functional architecture and can be used to evaluate novel sources of individual differences.

  PublisherDOI

• BrainEffeX: A Web App for Exploring fMRI Effect Sizes

  2025-05-01

  preprintOpen accessSenior author

  Effect size estimation is crucial for power analyses and experimental design, but posesunique challenges in fMRI research due to the complexity of the data and analysistechniques. Here, we introduce an interactive web application for exploring fMRI effect maps(neuroprismlab.shinyapps.io/BrainEffeX). We utilized large fMRI datasets to obtain precisevoxel-wise and multivariate effect size estimates from “typical” fMRI study designs:brain-behavior correlation, task vs. rest, and between-group analyses of functionalconnectivity and task-based activation maps. The app is intentionally designed as a growingresource, and we welcome contributions of large (n > 500) datasets.

  PublisherOA PDFDOI

• When no answer is better than a wrong answer: A causal perspective on batch effects

  Imaging Neuroscience · 2025-01-01 · 3 citations

  articleOpen access

  Batch effects, undesirable sources of variability across multiple experiments, present significant challenges for scientific and clinical discoveries. Batch effects can (i) produce spurious signals and/or (ii) obscure genuine signals, contributing to the ongoing reproducibility crisis. Because batch effects are typically modeled as classical statistical effects, they often cannot differentiate between sources of variability due to confounding biases, which may lead them to erroneously conclude batch effects are present (or not). We formalize batch effects as causal effects, and introduce algorithms leveraging causal machinery, to address these concerns. Simulations illustrate that when non-causal methods provide the wrong answer, our methods either produce more accurate answers or "no answer," meaning they assert the data are inadequate to confidently conclude on the presence of a batch effect. Applying our causal methods to 27 neuroimaging datasets yields qualitatively similar results: in situations where it is unclear whether batch effects are present, non-causal methods confidently identify (or fail to identify) batch effects, whereas our causal methods assert that it is unclear whether there are batch effects or not. In instances where batch effects should be discernable, our techniques produce different results from prior art, each of which produce results more qualitatively similar to not applying any batch effect correction to the data at all. This work, therefore, provides a causal framework for understanding the potential capabilities and limitations of analysis of multi-site data.

  PublisherOA PDFDOI

• Increasing Accountability and Compliance with Robot Advice

  Journal of Marketing · 2025-08-08 · 1 citations

  articleOpen accessSenior author

  Service robots on organizational frontlines, notably in health and elderly care settings, promise to tackle staff shortages. In such service contexts, compliance is crucial for consumer well-being, but compliance with robot advice remains problematically low. This research explores how the source of robot advice affects compliance in human–robot interactions. In six studies, including four field studies with real human–robot interactions, the authors demonstrate that consumers are more likely to comply with advice given by a robot service provider when the source of advice is a human rather than the robot itself. This is because a human source of robot advice increases the feeling of accountability, or the expectation that one might need to justify one's actions to others, which is more difficult to achieve with only robot social presence. In turn, this fosters advice adherence, which also persists over time across repeated interactions. However, when the robot embeds social cues in the advice, the difference in accountability and compliance between robot-only and robot advice with a human source attenuates. These insights hold enormous promise, especially for health care practitioners, institutions, and consumers for whom increased compliance can lead to better health outcomes, reduced hospital readmissions, improved recovery, and elevated well-being.

  PublisherDOI

• Greetings from the new JAMS editorial team

  Journal of the Academy of Marketing Science · 2025-01-01 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

• Author response: Trends in Self-citation Rates in High-impact Neurology, Neuroscience, and Psychiatry Journals

  2025-04-24

  peer-reviewOpen access

  Citation metrics influence academic reputation and career trajectories. Recent works have highlighted flaws in citation practices in the Neurosciences, such as the under-citation of women. However, self-citation rates—or how much authors cite themselves—have not yet been comprehensively investigated in the Neurosciences. This work characterizes self-citation rates in basic, translational, and clinical Neuroscience literature by collating 100,347 articles from 63 journals between the years 2000-2020. In analyzing over five million citations, we demonstrate four key findings: 1) increasing self-citation rates of Last Authors relative to First Authors, 2) lower self-citation rates in low- and middle-income countries, 3) gender differences in self-citation stemming from differences in the number of previously published papers, and 4) variations in self-citation rates by field. Our characterization of self-citation provides insight into citation practices that shape the perceived influence of authors in the Neurosciences, which in turn may impact what type of scientific research is done and who gets the opportunity to do it.

  PublisherDOI

• Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-13

  preprintOpen access

  A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on brain connectivity data are increasingly used as tools for predicting behavioral phenotypes 1,2, enhancing precision medicine 3,4, and improving generalizability compared to traditional MRI studies 5. However, the high dimensionality of brain connectivity data makes model interpretation challenging 6. Prevailing practices within the field rely on sparsely selected brain connectivity features, implicitly interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Here, we show that commonly overlooked brain connectivity features can achieve similar prediction accuracies while yielding markedly different neurobiological interpretations. Using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that this phenomenon is widespread across cognitive, developmental, and psychiatric phenotypes. It extends to both functional connectivity (fMRI) and structural (DTI) connectomes and remains evident even in external validation. These findings suggest that common practices may lead to feature overinterpretation and a misrepresentation of the neurobiological bases of brain-behavior associations. Such interpretations present only the "tip of the iceberg" when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results point to the possibility that multiple neurobiologically distinct models may exist for the same phenotype, with implications for identifying meaningful subtypes within clinical and research populations.

  PublisherOA PDFDOI

Recent grants

• Improving Reliability and Validity of fMRI Statistical Methods

  NIH · $43k · 2018–2019

• Constrained Network-Based Multiple Comparison Correction

  NIH · $84k · 2019–2022

• Empirical Power Analysis Tool for fMRI

  NIH · $123k · 2022–2023

Frequent coauthors

• C. B. Macpherson

  Massey University

  88 shared

• W Waines

  ECW Press (Canada)

  88 shared

• V. W. Bladen

  84 shared

• Dustin Scheinost

  Yale University

  80 shared

• Martin Sharp

  72 shared

• K W I L S O N

  Holland Bloorview Kids Rehabilitation Hospital

  72 shared

• B. S. Keirstead

  72 shared

• B Brouillette

  McMaster University

  72 shared