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Science

Advancing Methods

Making the Science of the Brain Reliable and Reproducible

Advancing Methods

The scientific method requires that hypotheses be robustly tested so that they can be built upon. But in mental health research, there is a crisis of reproducibility. Sample sizes are frequently too small and measurements too imprecise. Different tools yield different results. And, all too often, one analytic approach suggests a different conclusion from another.

Treatments won’t improve if the science behind them can’t be trusted. That’s why we’re focused on advancing the methods used in brain research. We’re developing analytical tools that allow scientists to pool data for greater impact. And we’re building neuroimaging tools and processes that gain deeper insights into brain signals, consistently detect meaningful differences, and strengthen the science behind mental health treatments.

Learn more about how we’re moving toward these crucial goals at our Computational Neuroimaging Lab.

Featured News

How Differences in Processing Reliability Can Hinder Advancement in Neuroimaging

Examining how cross-tool differences can distort our ability to detect individual variations, and advance the field.

How

Embracing Computational Errors to Create More Predictive and Generalizable Biomarkers

Exploring how computational errors can be leveraged to improve models of brain networks.

Embracing

Computational Errors Could Have a Negative Impact on the Ability to Study Brain Networks

Identifying the effects that random unavoidable computing errors have on developing reliable models of brain-phenotype relationships.

Computational

Preprint on Phenotypic Reliability

Achieving better biomarker discovery for a fraction of the cost of large-scale samples.

Preprint

Groundbreaking Study Predicts Mental Health Outcomes via Pandemic-Induced Stressors

Evaluating the impact of the Covid-19 pandemic on the mental health of adults and children.

Groundbreaking