Building a Science of Individual Differences from fMRI

• Published in: Trends in cognitive sciences Vol. 20; no. 6; pp. 425 - 443
• Main Authors: Dubois, Julien, Adolphs, Ralph
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.06.2016
• Subjects: BOLD fMRI; Brain - diagnostic imaging; Brain Mapping - methods; Humans; individual differences; Individuality; Magnetic Resonance Imaging; Neurology; neurometrics; prediction; Psychiatry; reliability; Reproducibility of Results; validity; prediction; validity; neurometrics; BOLD fMRI; reliability; individual differences
• ISSN: 1364-6613; 1879-307X
• DOI: 10.1016/j.tics.2016.03.014

To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity, reliability and statistical assessment that arise when the focus shifts to individual subjects and that are applicable also to other imaging modalities. We emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability as well as sources of within-subject variability. Interpretation of fMRI data at the level of individual brains is essential for characterizing brain function in health and disease. Two core challenges are validity (do we measure what we intend to measure?) and reliability (is our measure stable in the face of variations that should not matter?) of fMRI-derived individual differences; these challenges can be partly addressed with recent tools. Interpretation of single-subject fMRI measures relies on establishing a relationship with an independent measure in the same subjects. Out-of-sample prediction should be used over correlation analysis. Accumulation of large samples through consortia and data sharing, as well as careful attention to statistical power issues, are crucial for reproducible research. Whole-brain characterization in naturalistic conditions, such as while watching a movie or listening to a story, may provide an alternative to resting-state data that permits a rich link to sensory and semantic stimulus variables.