James V. Haxby

This paper introduces a method to align subcortical brain connectivity across individuals, revealing shared fine-scale functional organization that was previously obscured by anatomical variability.

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 shows that using age-specific templates improves hyperalignment accuracy in brain data across different age groups.

The paper evaluates how action, agent, and scene features predict cortical activity during natural vision and finds action features capture the most unique variance across the cortex.

The paper shows that behavioral models of action purpose and social content better predict neural representational geometry than other models in the action observation network.

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.

This study examines reliability, heritability, and behavioral relevance of resting-state functional connectivity at different spatial scales in children, finding that fine-scale connectivity is more reliable but less heritable than coarse-scale connectivity.

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.

This paper introduces hybrid hyperalignment, a method that aligns both neural response and functional connectivity data across brains using a single model, showing improved alignment for both types of data compared to previous methods.

This study predicts general intelligence using fine-grained cortical connectivity patterns, finding stronger predictions than coarse-grained patterns.

The paper explores the relationship between smartphone usage patterns and brain connectivity in college students, finding a positive link between screen time and resting-state functional connectivity in regions associated with depression.

This study uses hyperalignment to examine individual differences in cortical functional architecture, showing it improves reliability across functional indices by resolving topographic idiosyncrasies.

The paper develops a method for generalizing semantic encoding models across individuals using hyperalignment, showing it outperforms anatomical normalization and within-subject models.

This study presents fMRI data from participants viewing naturalistic animal videos in two task contexts, showing how task demands affect neural representations of complex stimuli.

The paper develops a high-dimensional model of the human connectome that captures shared fine-scale structure, revealing it as a major component of connectivity previously overlooked by coarse-scale models.

The paper investigates how attention affects the discriminability of distributed neural representations for behavior and taxonomy in humans.