Guo Jiahui

Hyperface provides a naturalistic fMRI dataset for studying human face processing under ecologically valid conditions and enables benchmarking computational models against human brain responses.

This study uses response hyperalignment to show shared representational spaces in macaque fMRI data despite individual differences in functional topographies.

This study uses hyperalignment to estimate individualized functional topographies in developmental prosopagnosia without localizer scans, showing high fidelity and cross-group generalization.

The paper examines the relationship between gyral crown height and face-selective regions in the fusiform gyrus, finding reduced crown height correlates with impaired face recognition in developmental prosopagnosia and replicates this in a large neurotypical sample.

The paper identifies a sulcal network in the posteromedial cortex associated with face processing and finds structural differences between neurotypical individuals and those with developmental prosopagnosia.

The paper introduces a new cortical surface template, onavg, which provides uniform sampling of the cortex and improves classification accuracy and computational efficiency compared to existing templates.

The paper estimates individualized cortical functional topographies from naturalistic movies across different participants, scanners, and institutes with high fidelity.

The paper introduces the Individualized Neural Tuning (INT) model, which provides a fine-grained, reliable, and generalizable measure of brain functional organization across individuals.

This study compares representational geometries of deep convolutional neural networks, human cognition, and brain activity during naturalistic face processing and finds that late-intermediate layers better match cognitive and neural geometries than late layers.

The paper examines the relationship between the length of the mid-fusiform sulcus and face perception ability in neurotypical controls and individuals with developmental prosopagnosia.

The paper describes and shares an fMRI dataset from 25 subjects watching 'The Grand Budapest Hotel', focusing on social interactions and face processing.