Gillette, K. D. | Search bioRxiv

bioRxiv2026

White-matter connectivity shapes visual and semantic representations in the aging brain

Multiple lines of evidence indicate that aging is associated with a reduction in the quality of sensory information coupled with increased reliance on semantic information. Consistent with this evidence, functional MRI studies have repeatedly found that, compared to younger adults, older adults show weaker visual representations but stronger semantic representations. However, the factors that engender this visuo-semantic representational shift (VSRS) remain unclear. Here, we test whether structural connectivity within the representational network predicts these shifts. In the face of typical age-related declines in visual memory and structural connectivity, we find that structural connectivity (i) mediates age-related decline in perceptual representation, and (ii) supports semantic and perceptual mnemonic representations in older adults. These findings outline a potential mechanism of age-related perceptual processing declines and highlight the adaptability of the memory system with age.

neuroscience↗

bioRxiv2022

Through a Dog's Eyes: fMRI Decoding of Naturalistic Videos from Dog Cortex

Recent advancements using machine learning and fMRI to decode visual stimuli from human and nonhuman cortex have resulted in new insights into the nature of perception. However, this approach has yet to be applied substantially to animals other than primates, raising questions about the nature of such representations across the animal kingdom. Here, we used awake fMRI in two domestic dogs and two humans, obtained while each watched specially created dog-appropriate naturalistic videos. We then trained a neural net (Ivis) to classify the video content from a total of 90 minutes of recorded brain activity from each. We tested both an object-based classifier, attempting to discriminate categories such as dog, human and car, and an action-based classifier, attempting to discriminate categories such as eating, sniffing and talking. Compared to the two human subjects, for whom both types of classifier performed well above chance, only action-based classifiers were successful in decoding video content from the dogs. These results demonstrate the first known application of machine learning to decode naturalistic videos from the brain of a carnivore and suggest that the dogs-eye view of the world may be quite different than our own.

animal behavior and cognition↗