Department of Statistics

Commentary on The Future of the Textbook.

The study of the brain is a compelling example of the power of convergent science. Over the last few decades, advances in neuroscience techniques and experimentation, as well as in data science tools to analyze the resulting data, have dramatically furthered our understanding of fundamental brain functions. Historically, it has been common for analytical approaches to have a considerable lag in development following the availability of new neuroscience techniques. However, this relationship has not simply been unidirectional, as there have been examples in which analytical developments have directly led to new scientific questions and experiments. Here we review how this interplay between neuroscience and data science advances has unfolded in the past and into the present, with a focus on electrophysiology and calcium imaging. Applying lessons learned from the past and present, we then discuss expected developments, challenges, and opportunities in the future. We end by providing recommendations on how to foster the necessary team science approach to continue the advancement of research at the intersection of neuroscience and data science, which we call neurodatascience, toward a sustainable future.

In this article, we examine the foundations of a rich and diverse literature in economics and derive the likelihood function of simultaneous equation models for discrete data as the invariant distribution of a suitably specified Markov process. This formulation offers a well-defined reduced form of the model and dispenses with the need for controversial recursivity requirements and ad hoc indeterminacy rules. The derivation resolves puzzling paradoxes highlighted in earlier work and shows that the likelihood is unique, proper, coherent, complete, and theoretically grounded in conditional distribution modeling – a framework that has yet to be popularized in economics. We note possible extensions and relevant links with other models, comment on computational issues, and implement the methodology in three empirical applications involving female labor force participation, the interactions between health and wealth, and the interplay between banks’ lending practices and their reliance on assistance from the lender of last resort during the Great Depression.

Acupuncture is an ancient practice that was developed within the framework of traditional Chinese medicine. While acupuncture has been recently proposed as a therapy for Alzheimer’s disease (AD), acupuncture effects are not well understood in terms of neural mechanisms. Here, we review and examine the studies that used AD mouse models and analyze the experiments where researchers administered electroacupuncture (EA) to AD mice to assess the potential therapeutic impact of acupuncture on disease pathology and cognitive function in controlled laboratory settings. We analyzed 29 relevant PubMed articles published between January 2014 and July 2025. Our results reveal that EA significantly reduces both amyloid-beta (Aβ) and phosphorylated tau (p-tau) levels and neuroinflammatory biomarkers, including molecular signatures for activated microglia and astrocytes in the brain. EA also enhances cognitive functions. While no study directly compared acupoint strategies, the indirect comparisons in our network analysis suggest that GV20 has potential as a therapeutic target for AD. Our present meta-analysis and review of literature add to the evidence of integrative health practices for acupuncture-based Alzheimer’s disease treatment.

Background: Digital technologies offer tremendous potential to predict dysregulated mood and behavior within an individual's environment, and in doing so can support the development of new digital health interventions. However, no prediction models have been built in trauma-exposed populations that leverage real-world data.Objective: This project aimed to determine if wearable-derived physiological data can predict anger intensity in trauma-exposed adults.Method: Heart rate variability (i.e. a commercial wearable stress score) was combined with ecological momentary assessment (EMA) data collected over 10 days (n = 84). Five summary measures from stress scores collected 10 min prior to each EMA were selected using factor analysis of 24 candidates.Results: A high area under the receiver operating curve (AUC) was found for a logistic mixed effects model including these measures as predictors, ranging 0.761 (95% CI:0.569-0.921) to 0.899 (95% CI:0.784-0.980) across cross-validation methods.Conclusions: While the predictive performance may be overly optimistic due to the outcome prevalence (13.8%) and requires replication with larger datasets, our promising findings have significant methodological and clinical implications for researchers looking to build novel prediction and treatment approaches to respond to posttraumatic mental health.

The EZ-diffusion model is a simplification of the popular drift diffusion model of choice response times that allows researchers to calculate diffusion model parameters directly from data with no need for expensive computations. The EZ-diffusion model is based on a system of equations in which the diffusion model’s drift rate, boundary separation, and nondecision time parameters are jointly used to predict three summary statistics (the accuracy rate and the mean and variance of the correct response times). These equations can then be inverted to obtain estimators for the three parameters from these summary statistics. Here, we describe a probabilistic formulation of the EZ-diffusion model that can serve as a hyper-efficient proxy model to the drift diffusion model. The new formulation is based on sampling distributions of summary statistics and consists only of normal and binomial distributions. It can easily be implemented in any probabilistic programming language. We demonstrate the validity of the proxy model through extensive simulation studies and provide multiple examples (via https://osf.io/bzkpn/), including an implementation in JASP. We conclude that, although the recovery of some parameters with the proxy model is biased, the recovery of regression parameters is good, making the method useful for cognitive psychometrics (i.e., explanatory cognitive modeling). Casting the EZ-diffusion model in the broad family of Bayesian generative models allows us to benefit from mature implementations, practical workflows, and powerful extensions that are not possible without a probabilistic implementation and not feasible with the regular drift diffusion model. Code and example applications are provided via https://osf.io/bzkpn/.

Micro-randomized trials (MRTs) play a crucial role in optimizing digital interventions. In an MRT, each participant is sequentially randomized among treatment options hundreds of times. While the interventions tested in MRTs target short-term behavioral responses (proximal outcomes), their ultimate goal is to drive long-term behavior change (distal outcomes). However, existing causal inference methods, such as the causal excursion effect, are limited to proximal outcomes, making it challenging to quantify the long-term impact of interventions. To address this gap, we introduce the distal causal excursion effect (DCEE), a novel estimand that quantifies the long-term effect of time-varying treatments. The DCEE contrasts distal outcomes under two excursion policies while marginalizing over most treatment assignments, enabling a parsimonious and interpretable causal model even with a large number of decision points. We propose two estimators for the DCEE-one with cross-fitting and one without-both robust to misspecification of the outcome model. We establish their asymptotic properties and validate their performance through simulations. We apply our method to the HeartSteps MRT to assess the impact of activity prompts on long-term habit formation. Our findings suggest that prompts delivered earlier in the study have a stronger long-term effect than those delivered later, underscoring the importance of intervention timing in behavior change. This work provides the critically needed toolkit for scientists working on digital interventions to assess long-term causal effects using MRT data.

BackgroundWhile recent studies have found associations between markers of autoimmunity/inflammation and cognitive performance in individuals aged 60-90, these findings remain unexplored in individuals aged 90 and above.ObjectiveTo examine the prevalence of autoimmune antibodies and raised inflammatory markers and their associations with cognition in participants aged 90 + .MethodsWe included participants with serological testing from The 90+ Study, a community-based longitudinal study in southern California. For measures of autoimmunity, we evaluated antinuclear, antineutrophil cytoplasmic (ANCA), rheumatoid factor, double stranded DNA, antithyroglobulin, and thyroid peroxidase antibodies. For inflammatory markers, we examined interleukin-6 (IL-6) and erythrocyte sedimentation rate (ESR). To examine the relationship between autoimmune antibodies and inflammatory markers with cognitive performance, we ran linear mixed effects models.ResultsAmong 201 participants (mean age 94.8 years, 56.7% female, 93.5% white, and 4.5% with rheumatologic illness), autoimmune antibodies were positive in 70.2%. Also, among 142 participants with test results, elevated inflammatory markers were detected in 76.8%. Linear mixed effects model analyses revealed an association between higher levels of ANCA (p = 0.04), IL-6 (p = 0.01), and ESR (p = 0.01) and lower global cognitive scores. In a subset of participants with amyloid PET (n = 173), results remained significant even after accounting for amyloid burden.ConclusionsAutoimmune antibodies and raised inflammatory markers were highly prevalent in a community cohort of individuals aged 90 + . Our results suggest that increased prevalence of autoimmunity and inflammation might be associated with worse cognitive performance in this age group, independent of amyloid.