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1. NeuroQuery, comprehensive meta-analysis of human brain mapping

   https://doi.org/10.7554/eLife.53385  

   Dockès, Jérôme; Poldrack, Russell A; Primet, Romain; Gözükan, Hande; Yarkoni, Tal; Suchanek, Fabian; Thirion, Bertrand; Varoquaux, Gaël (March 2020, eLife)

   Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms. The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Existing meta-analysis methods perform statistical tests on sets of publications associated with a particular concept. Thus, large-scale meta-analyses only tackle single terms that occur frequently. We propose a new paradigm, focusing on prediction rather than inference. Our multivariate model predicts the spatial distribution of neurological observations, given text describing an experiment, cognitive process, or disease. This approach handles text of arbitrary length and terms that are too rare for standard meta-analysis. We capture the relationships and neural correlates of 7547 neuroscience terms across 13 459 neuroimaging publications. The resulting meta-analytic tool, neuroquery.org, can ground hypothesis generation and data-analysis priors on a comprehensive view of published findings on the brain. 

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2. Granger causality for mixed time series generalized linear models: A case study on multimodal brain connectivity

   https://doi.org/10.1214/26-AOAS2185  

   Piancastelli, Luiza_S C; Barreto-Souza, Wagner; Fortin, Norbert J; Cooper, Keiland W; Ombao, Hernando (June 2026, The Annals of Applied Statistics)

   This paper is motivated by neuroscience studies aimed at understanding causal or predictive interactions between nodes in a brain network using multimodal brain activity data. To assess Granger causality, we introduce a flexible framework through a general class of models that accommodate mixed types of data (binary, count, continuous and positive components) formulated in a generalized linear model (GLM) fashion. To conduct statistical inference for causality, we propose a Bayesian mixed time series model that incorporates spike-and-slab priors on selected parameters. This framework enables effective selection of causal ordering and offers robust uncertainty quantification. The proposed methods are then applied to brain activity data, including both spike train and local field potential (LFP) activity, recorded as animals (rats) performed a complex sequence memory task. The proposed methodology provides critical insights into the causal relationship between band-specific spectral power in the LFP and subsequent spiking activity. Specifically, power in the LFP beta band is predictive of spiking activity 300 milliseconds later, providing a novel analytical tool for this area of emerging interest in neuroscience and demonstrating its usefulness and flexibility in the study of causality in general. 

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3. Better Accuracy for Better Science . . . Through Random Conclusions

   https://doi.org/10.1177/17456916231182097  

   Davis-Stober, Clintin P; Dana, Jason; Kellen, David; McMullin, Sara D; Bonifay, Wes (January 2024, Perspectives on Psychological Science)

   Pleskac, Tim (Ed.)

   Conducting research with human subjects can be difficult because of limited sample sizes and small empirical effects. We demonstrate that this problem can yield patterns of results that are practically indistinguishable from flipping a coin to determine the direction of treatment effects. We use this idea of random conclusions to establish a baseline for interpreting effect-size estimates, in turn producing more stringent thresholds for hypothesis testing and for statistical-power calculations. An examination of recent meta-analyses in psychology, neuroscience, and medicine confirms that, even if all considered effects are real, results involving small effects are indeed indistinguishable from random conclusions. 

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4. High-Dimensional Inference for Generalized Linear Models with Hidden Confounding

   Ouyang, J; Tan, KM; Xu, G (August 2023, Journal of Machine Learning Research)

   Statistical inferences for high-dimensional regression models have been extensively studied for their wide applications ranging from genomics, neuroscience, to economics. However, in practice, there are often potential unmeasured confounders associated with both the response and covariates, which can lead to invalidity of standard debiasing methods. This paper focuses on a generalized linear regression framework with hidden confounding and proposes a debiasing approach to address this high-dimensional problem, by adjusting for the effects induced by the unmeasured confounders. We establish consistency and asymp- totic normality for the proposed debiased estimator. The finite sample performance of the proposed method is demonstrated through extensive numerical studies and an application to a genetic data set. 

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5. Memphis NeuroSTART Program: Promoting Student Success and Increasing the Diversity of Applicants to Neuroscience Graduate Programs

   https://doi.org/10.59390/YGCW6032  

   Sable, Helen JK; Lester, Deranda B (August 2024, Journal of Undergraduate Neuroscience Education)

   With grant support from the Research Experience for Undergraduates (REU) program funded by the National Science Foundation (NSF) and the Awards to Stimulate and Support Undergraduate Research Experiences (ASSURE) program funded by the Department of Defense (DoD) Air Force Office of Scientific Research (AFOSR), we established a program intended to increase the number of underrepresented racial and ethnic minority (URM) and first-generation undergraduate students successfully applying to neuroscience and other STEM-related graduate programs. The Neuroscience Techniques and Research Training (NeuroSTART) Program aimed to increase the number of undergraduate students from the Memphis area involved in behavioral neuroscience research. In this two-semester program, students completed an empirical research project in a neuroscience lab, received individual mentoring from neuroscience faculty, became part of a STEM network, presented at research conferences, and attended specialized professional development seminars. In two cohorts of 15 students, 4 are PhD students in neuroscience-related programs or in medical school (27%), 4 are employed in neuroscience-related research facilities (27%), 3 are employed as clinical assistants (20%), and 1 is employed in the IT field (7%). The remaining three recently graduated and are planning a gap year prior to applying for admission to grad/medical school. The Memphis NeuroSTART program has provided valuable training to participants, making them competitive applicants for jobs in the health sciences and for admittance into graduate neuroscience programs. By providing this training to first-generation and URM students, the broader impact of this program was an increase in the diversity of the health sciences workforce, particularly those specializing in neuroscience-related research and treatment. 

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