Abstract
We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience.
Highlights
We consider between-subject variance in brain function as data rather than noise
Celebrating Variability No two human brains are identical, with variability in brain anatomy and function emerging from how each individual brain is genetically built and shaped by its intimate interaction with the environment
Methods for measuring intersubject variability have mainly focused on developing models of normal brain function that allow abnormality to be quantified in patient populations
Summary
We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. Methods for measuring intersubject variability have mainly focused on developing models of normal brain function (i.e., norms) that allow abnormality to be quantified in patient populations This quantification of norms relies on a reductionist framework that aims to collapse the data across the subject dimension and focus on the significant common (i.e., overlapping) or mean effects (Box 1). The search for the mean group effect (i.e., central tendency), typically defined as the ultimate representative subject, implicitly treats variability that cannot be explained by any experimental manipulation as a nuisance, noise, or measurement error This ignores many relevant sources of intersubject variability, including the use of different cognitive strategies for the same task [1,2,3] (Box 2), differences in learning or subjective judgment [4,5], and the inherent normal variance in ability and capacity [6]. Task manipulation: methodical way to implement stimulus or task changes in neuroimaging experiments
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