We all wonder how similar our individual thoughts are. We are fairly sure that our brains are not identical, not like the little worm C. elegans with its 302 neurons and all the synapses mapped. But we are also fairly sure that we have great similarity in the architecture of our brains; we all have the same little motor homunculus across the top of our brains that we see in illustrations. We all have the primary optical perception in predictable locations at the back of the brain. It seems pretty clear that different aspects of brain function have different levels of individuality content of memories would be almost completely individual while control of muscles would be quite standardized.
Some light has been shone on the subject by J.A. Clithero and others in NeuroImage (see citation). They have compared fMRI scans ‘within-participants’ with those ‘cross-participants’. Where in the brain is there similar BOLD activity for a type of event in a single person and where is there similarity between people?
Nearly all MVPA studies that employ classifiers build an independent classification model for each participant, based on the trial-to- trial variability in the fMRI signal. This approach is well-suited to identify brain regions that play a consistent functional role within- participants, but it cannot make claims about common cross- participant representation. While relatively few studies have adopted the latter approach, some early applications have targeted deception , different object categories, mental states that are consistent across a wide variety of tasks , attention, biomarkers for psychosis, and Alzheimer’s disease . To date, however, no study has systematically evaluated whether within- and cross-participant analyses provide distinct information about brain function.
This is the missing perspective that they were looking at. Here is their abstract:
Analyzing distributed patterns of brain activation using multivariate pattern analysis (MVPA) has become a popular approach for using functional magnetic resonance imaging (fMRI) data to predict mental states. While the majority of studies currently build separate classifiers for each participant in the sample, in principle a single classifier can be derived from and tested on data from all participants. These two approaches, within- and cross-participant classification, rely on potentially different sources of variability and thus may provide distinct information about brain function. Here, we used both approaches to identify brain regions that contain information about passively received monetary rewards (i.e., images of currency that influenced participant payment) and social rewards (i.e., images of human faces). Our within- participant analyses implicated regions in the ventral visual processing streamincluding fusiform gyrus and primary visual cortexand ventromedial prefrontal cortex (VMPFC). Two key results indicate these regions may contain statistically discriminable patterns that contain different informational representations. First, cross-participant analyses implicated additional brain regions, including striatum and anterior insula. The cross-participant analyses also revealed systematic changes in predictive power across brain regions, with the pattern of change consistent with the functional properties of regions. Second, individual differences in classifier performance in VMPFC were related to individual differences in preferences between our two reward modalities. We interpret these results as reflecting a distinction between patterns showing participant-specific functional organization and those indicating aspects of brain organization that generalize across individuals.
Clithero, J., Smith, D., Carter, R., & Huettel, S. (2011). Within- and cross-participant classifiers reveal different neural coding of information NeuroImage, 56 (2), 699-708 DOI: 10.1016/j.neuroimage.2010.03.057