Biology subjects

Dilks, D. D.

Publications and source records attributed to Dilks, D. D..

3 recordsLinked to original sources

bioRxiv2026

Expanded Group-Constrained Parcels Enhance Coverage and Reproducibility of the Scene-Selective Cortex

A common approach for investigating high-level visual cortex with functional magnetic resonance imaging (fMRI) is to define regions of interest (ROIs) in individual participants using functional activation clusters and anatomical landmarks. Although highly productive, this approach requires manual decisions about which clusters correspond to specific canonical regions, limiting reproducibility and posing challenges in populations with lower signal-to-noise ratios, such as children. The Group-Constrained Subject-Specific (GSS) approach reduces this subjectivity by using group-level parcels to constrain subject-specific functional ROI definition. However, the original GSS parcel set provides limited coverage of the occipital place area (OPA) and does not include more recently characterized scene-selective regions. Here, we introduce an updated and expanded set of GSS parcels for scene-selective cortex. Using a larger adult sample and dynamic scene stimuli, we generated updated parcels for OPA, parahippocampal place area (PPA), and retrosplenial complex (RSC), and for the first time, delineated a parcel for a newly discovered scene-selective region in the superior parietal lobule (superior place area; SPA). We evaluated these parcels in independent adult and pediatric datasets by testing whether they improve cross-subject coverage while preserving functional selectivity. The updated OPA parcel increased cross-subject coverage relative to the original parcel by Julian and colleagues. Moreover, ROIs defined using the updated parcels showed equal or greater scene selectivity across OPA, PPA, and RSC, indicating improved functional ROI definition without sacrificing specificity. Across scene-selective regions, the updated parcels reliably identified scene-selective cortex and reproduced canonical response profiles and in pediatric data. These parcels provide more complete and reliable coverage of the scene-processing network, supporting objective and reproducible ROI definition across adult and pediatric fMRI datasets. HighlightsO_LIExpanded group-constrained parcels improve coverage of scene-selective cortex C_LIO_LIDynamic stimuli yield improved cross-subject overlap for OPA C_LIO_LINew parcel introduced for the scene-selective region in the superior parietal lobule, now called superior place area (SPA) C_LIO_LIUpdated parcels reproduce canonical response profiles in adult data C_LIO_LIParcels reliably identify scene-selective voxels in pediatric datasets C_LI

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↗

bioRxiv2019

A dual role for shape skeletons in human vision: perceptual organization and object recognition

Shape perception is crucial for object recognition. However, it remains unknown exactly how shape information is represented, and, consequently, used by the visual system. Here, we hypothesized that the visual system represents "shape skeletons" to both (1) perceptually organize contours and component parts into a shape percept, and (2) compare shapes to recognize objects. Using functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA), we found that a model of skeletal similarity explained significant unique variance in the response profiles of V3 and LO, regions known to be involved in perceptual organization and object recognition, respectively. Moreover, the skeletal model remained predictive in these regions even when controlling for other models of visual similarity that approximate low- to high-level visual features (i.e., Gabor-jet, GIST, HMAX, and AlexNet), and across different surface forms, a manipulation that altered object contours while preserving the underlying skeleton. Together, these findings shed light on the functional roles of shape skeletons in human vision, as well as the computational properties of V3 and LO.

neuroscience↗