The Low-Rank Brain: Why fMRI Encoding-Model Predictions Can Mislead Decoding and Localization

Predictions from fMRI encoding models are increasingly analyzed as if they were measured brain activity: decoded, correlated, and compared across cortical regions. But a predicted response is a function of stimulus features, and its many vertices need not carry independent or regionally specific information. We examine this with two audio-to-fMRI encoders applied to 8,625 fifteen-second solo-piano clips spanning 12 composers and three stylistic eras. The predictions of the first, TRIBE v2, span 20,484 cortical vertices, yet 6, 9, and 19 standardized principal components explain 90%, 95%, and 99% of their variance. A linear ridge encoder, fit independently to one subject’s natural-speech fMRI and applied out of domain, is lower-rank still (3, 6, and 16 components), suggesting that low rank is not specific to one encoder, although this exploratory comparison varies several factors at once. Era is linearly decodable from TRIBE’s predictions and transfers to held-out composers under leave-one-composer-out (LOCO) cross-validation (balanced accuracy 0.469, chance 0.333; composer-permutation $p=0.004$), whereas the ridge predictions do not decode it (0.280). Yet when era is decoded within each of 22 cortical divisions of the HCP-MMP1.0 atlas, no division beats a size-matched random-vertex null (max-statistic family-wise $p=0.465$): the resulting map looks spatially organized, but no region carries more era information than an arbitrary vertex set of equal size. Encoding-model predictions can thus support decoding without supporting region-level localization. We recommend reporting the effective rank of predicted responses and testing region-level effects against size-matched random-vertex nulls before drawing spatial conclusions. Our claims concern downstream analyses of model predictions, not the validity of encoding models or of measured fMRI.