Robert Emmett Kelly

Null Hypothesis Significance Testing (NHST) remains the dominant paradigm for evaluation of empirical research findings in medicine and the social sciences despite concerns about frequent misinterpretations of those findings. Achievement of “statistical significance,” the goal of NHST, often beckons unrealistic conclusions. Helpful would be the addition of a broader, Bayesian perspective of research in terms of progressive readjustment of hypothesis credibility from all sources of evidence. For this purpose, the Hypothesis Race Model (HRM) provides an intuitive Bayesian approach that builds upon NHST-concepts, helping to correct misunderstandings with minimal reeducation. The HRM is an extension of the Bayesian approach by Ioannidis in 2005 that helped to explain “why most published research findings are false.” It is powerful enough to serve as the foundation for mathematical models to estimate and reduce the cost of empirical hypothesis testing.

Since its inception in May 2021, the Psychiatric Diseases Section of Brain Sciences has grown to include a staff of 28 academic editors with expertise related to clinical psychiatry, in addition to its supporting staff of managing and English-language editors

In the early 2010s, the “replication crisis” and synonymous terms (“replicability crisis” and “reproducibility crisis”) were coined to describe growing concerns regarding published research results too often not being replicable, potentially undermining scientific progress

Tinella et al.’s recent article

Over the past century, advancements in psychiatric treatments have freed countless individuals from the burden of life-long, incapacitating mental illness. These treatments have largely been discovered by chance. Theory has driven advancement in the natural sciences and other branches of medicine, but psychiatry remains a field in its “infancy”. The targets for healing in psychiatry lie within the realm of the mind’s subjective experience and thought, which we cannot yet describe in terms of their biological underpinnings in the brain. Our technology is sufficiently advanced to study brain neurons and their interactions on an electrophysiological and molecular level, but we cannot say how these form a single feeling or thought. While psychiatry waits for its “Copernican Revolution”, we continue the work in developing theories and associated experiments based on our existing diagnostic systems, for example, the Diagnostic and Statistical Manual of Mental Disorders (DSM), International Classification of Diseases (ICD), or the more newly introduced Research Domain Criteria (RDoC) framework. Understanding the subjective reality of the mind in biological terms would doubtless lead to huge advances in psychiatry, as well as to ethical dilemmas, from which we are spared for the time being.

Functional connectivity (FC) maps from brain fMRI data can be derived with dual regression, a proposed alternative to traditional seed‐based FC (SFC) methods that detect temporal correlation between a predefined region (seed) and other regions in the brain. As with SFC, incorporating nuisance regressors (NR) into the dual regression must be done carefully, to prevent potential bias and insensitivity of FC estimates. Here, we explore the potentially untoward effects on dual regression that may occur when NR correlate highly with the signal of interest, using both synthetic and real fMRI data to elucidate mechanisms responsible for loss of accuracy in FC maps. Our tests suggest significantly improved accuracy in FC maps derived with dual regression when highly correlated temporal NR were omitted. Single‐map dual regression, a simplified form of dual regression that uses neither spatial nor temporal NR, offers a viable alternative whose FC maps may be more easily interpreted, and in some cases be more accurate than those derived with standard dual regression.