Abstract
Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.
Highlights
Cognitive neuroscience uses functional brain imaging to probe the brain structures underlying mental processes
We propose a new approach for fMRI analysis, where a predictive model is used to extract the shared information from many studies together, while respecting their original paradigms
As brain mapping has progressed in exploring finer aspects of mental processes, the statistical power of studies has stagnated or even decreased [2]— sample size is increasing over years, it has not kept pace with the reduction of effect size
Summary
Cognitive neuroscience uses functional brain imaging to probe the brain structures underlying mental processes. Standard meta analyses can only address commonalities across studies, as they require casting mental manipulations in a consistent overarching cognitive theory They can bring statistical power at the cost of coverage and specificity in the cognitive processes. Our approach uses the specific psychological manipulations of each study and extracts shared information from the brain responses across paradigms. As a result, it improves markedly the statistical power of mapping brain structures to mental processes. It improves markedly the statistical power of mapping brain structures to mental processes We demonstrate these benefits on 35 functional-imaging studies, all analyzed to their individual experimental paradigm
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