Age differences in the neural representation of working memory revealed by multi-voxel pattern analysis.

Working memory function declines across the lifespan. Computational models of aging attribute such memory impairments to reduced distinctiveness between neural representations of different mental states in old age, a phenomenon termed dedifferentiation. These models predict that neural distinctiveness should be reduced uniformly across experimental conditions in older adults. In contrast, the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) model predicts that the distinctiveness of neural representations should be increased in older adults (relative to young adults) at low levels of task demand but reduced at high levels of demand. The present study used multi-voxel pattern analysis to measure the effects of age and task demands on the distinctiveness of the neural representations of verbal and visuospatial working memory. Neural distinctiveness was estimated separately for memory encoding, maintenance, and retrieval, and for low, medium, and high memory loads. Results from sensory cortex during encoding and retrieval were consistent with the dedifferentiation hypothesis: distinctiveness of visual cortical representations during these phases was uniformly reduced in older adults, irrespective of memory load. However, maintenance-related responses in prefrontal and parietal regions yielded a strikingly different pattern of results. At low loads, older adults showed higher distinctiveness than younger adults; at high loads, this pattern reversed, such that distinctiveness was higher in young adults. This interaction between age group and memory load is at odds with the dedifferentiation hypothesis but consistent with CRUNCH. In sum, our results provide partial support for both dedifferentiation- and compensation-based models; we argue that comprehensive theories of cognitive aging must incorporate aspects of both models to fully explain complex patterns of age-related neuro-cognitive change.