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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 & Publications

How Mapping Thoughts to Specific Activities Can Influence Our Overall Health and Well-Being

Researchers examine how our surroundings and everyday social interactions affect our thought patterns.


U.S. Study Finds Black Patients to Receive the Highest Rates of Psychotic Disorder Diagnoses

Scientists assess inequalities in the incidence of psychotic disorders among different racial and ethnic groups.


‘Mystical’ Experience Using Psychedelics May Improve Mental Health

Scientists study how LSD and natural psychedelic substances affect anxiety and depression symptoms.


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.


Embracing Computational Errors to Create More Predictive and Generalizable Biomarkers

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


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.


Preprint on Phenotypic Reliability

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


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.