Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research on Individual Differences.

Linking neurobiology to relatively stable individual differences in cognition, emotion, motivation, and behavior can require large sample sizes to yield replicable results. Given the nature of between-person research, sample sizes at least in the hundreds are likely to be necessary in most neuroimaging studies of individual differences, regardless of whether they are investigating the whole brain or more focal hypotheses. However, the appropriate sample size depends on the expected effect size.

Extracting interpretable signatures of whole-brain dynamics through systematic comparison.

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures.

Linking Genome-Wide Association Studies to Pharmacological Treatments for Psychiatric Disorders.

Importance: Large-scale genome-wide association studies (GWAS) should ideally inform the development of pharmacological treatments, but whether GWAS-identified mechanisms of disease liability correspond to the pathophysiological processes targeted by current pharmacological treatments is unclear.

Objective: To investigate whether functional information from a range of open bioinformatics datasets can elucidate the relationship between GWAS-identified genetic variation and the genes targeted by current treatments for psychiatric disorders.

Design, Setting, And Participants: Associations between GWAS-identified genetic variation and pharmacological treatment targets were investigated across 4 psychiatric disorders-attention-deficit/hyperactivity disorder, bipolar disorder, schizophrenia, and major depressive disorder.

Embracing variability in the search for biological mechanisms of psychiatric illness.

Despite decades of research, we lack objective diagnostic or prognostic biomarkers of mental health problems. A key reason for this limited progress is a reliance on the traditional case-control paradigm, which assumes that each disorder has a single cause that can be uncovered by comparing average phenotypic values of patient and control samples. Here, we discuss the problematic assumptions on which this paradigm is based and highlight recent efforts that seek to characterize, rather than minimize, the inherent clinical and biological variability that underpins psychiatric populations.