Ma Feilong

Shared Fine-Scale Functional Organization in the Human Subcortex Revealed by Connectivity Hyperalignment

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.

Brain Mechanisms of Lexicality in Bilingual Emotion Processing

This study uses fMRI to compare how native speakers and L2 learners process emotional prosody in real words vs. pseudowords, finding distinct brain activation patterns based on lexicality.

Hyperface: a naturalistic fMRI dataset for investigating human face processing

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

Hyperalignment Reveals Shared Information in Idiosyncratic Fine-Grained Movie fMRI Patterns in Macaques

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

Boosting Hyperalignment Performance with Age-specific Templates

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

Action features dominate cortical representation during natural vision

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.

A cortical surface template for human neuroscience

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.

Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development

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.

Cross-movie prediction of individualized functional topography

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

The Individualized Neural Tuning Model: Precise and generalizable cartography of functional architecture in individual brains

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

Modeling naturalistic face processing in humans with deep convolutional neural networks

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.

Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity

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.

The neural basis of intelligence in fine-grained cortical topographies

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

Reliable individual differences in fine-grained cortical functional architecture

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

Modeling semantic encoding in a common neural representational space

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

A computational model of shared fine-scale structure in the human connectome

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.