Biology subjects

Jiahui, G.

Publications and source records attributed to Jiahui, G..

10 recordsLinked to original sources

bioRxiv2026

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

Response hyperalignment (RHA) of fMRI patterns acquired during movie-viewing in 12 rhesus monkeys revealed shared representational spaces despite idiosyncratic functional topographies. RHA greatly improved inter-subject correlation of movie response time-series in occipital, temporal, and prefrontal cortices and afforded highly accurate between-subject decoding of movie time-points and estimation of idiosyncratic functional topographies. These results demonstrate that nonhuman primates share high-dimensional, high-capacity representational spaces encoded in idiosyncratic fine-grained functional topographies.

neuroscience↗

bioRxiv2026

Predicting Individualized Functional Topography in Developmental Prosopagnosia

Functional localizer scans have long served as the classic method for mapping individualized functional topographies, but they require dedicated scan time and can be difficult to implement in neuropsychological populations. Previous work has shown that individualized functional topographies can be estimated with high fidelity in typical participants using hyperalignment, but it remains unknown whether this approach generalizes to populations with functional deficits. Here, we tested this question in developmental prosopagnosia (DP), a neuropsychological condition characterized by severe face recognition impairments. Using two independent datasets that included both DP and control participants, we estimated individualized category-selective functional topographies from independent participants using hyperalignment derived from either a task-based scan or a naturalistic movie-viewing scan. Across datasets, whole-brain correlations and searchlight analyses showed that predicted topographies were highly similar to topographies estimated from participants own localizer data, especially in cortical areas with strong category-selective responses. Hyperalignment successfully recovered idiosyncratic features of category-selective topographies and consistently outperformed anatomical alignment. Importantly, predictions generalized across groups, such that individualized topographies in DPs could be estimated from control participants and vice versa. In addition, predicted topographies preserved the reduced face selectivity in DPs previously reported in the literature. These findings support a hyperalignment-based framework for estimating individualized functional topographies in neuropsychological populations without requiring separate localizer scans, and provide a foundation for integrating existing datasets to study the underlying neural basis in DP and other atypical populations.

neuroscience↗

bioRxiv2026

Hyperface: a naturalistic fMRI dataset for investigating human face processing

Faces convey information that guides social behavior, yet neuroimaging studies investigating human face processing typically use static images with small sets of identities under artificial conditions. Controlled designs limit our ability to characterize human face processing under naturalistic conditions or test whether computational models generalize beyond the laboratory. To address this gap, here we release hyperface, a naturalistic face viewing fMRI dataset designed to investigate human face processing in response to faces portrayed in videos mimicking more ecologically valid conditions. Twenty-one participants watched 707 unique face video clips that vary systematically in identity, gender, age, ethnicity, expression, and head orientation. Each clip was rated by independent observers, and pairwise similarity judgments were collected through a behavioral arrangement task. Technical validation confirms high data quality with low motion, high tSNR, and high inter-subject correlation in visual and face-processing regions. The hyperface dataset is part of a comprehensive experimental framework to investigate human face processing: all 21 participants also watched "The Grand Budapest Hotel," performed a dynamic face localizer task, and 10 participants completed an additional face identity task with personally familiar and visually familiarized faces. These datasets are publicly available and enable within-subject comparisons across paradigms. Together they provide a unique resource for characterizing human face processing under naturalistic conditions and for benchmarking computational models against human brain responses.

neuroscience↗

bioRxiv2025

Overlooked neuroanatomical markers of face processing and developmental prosopagnosia in posteromedial cortex

Recent functional imaging studies implicate human posteromedial cortex (PMC)--composed of the posterior cingulate cortex, the precuneus, and retrosplenial cortex--in face processing. Separately, anatomical studies have identified previously overlooked cortical folds (sulci) in PMC associated with higher-level cognitive abilities. Here, we tested whether these newly identified sulci support face processing in neurotypical individuals and individuals with developmental prosopagnosia (DP). After manually labeling 1,642 sulci in 164 hemispheres, we first identified a gradient of face selectivity along the anterior-posterior axis of PMC that was consistent across three samples, including DP individuals. Second, we discovered a new anatomical locus in PMC that differed structurally and functionally between neurotypical and DP individuals. Finally, data-driven analysis revealed that right-hemisphere PMC sulcal morphology was associated with face recognition ability. These findings reveal a sulcal network in PMC that supports face processing, and they identify the first structural neuroanatomical marker of face processing deficits in PMC.

neuroscience↗

bioRxiv2025

Gyral crowns contribute to the cortical infrastructure of human face processing

Neuroanatomical features across spatial scales contribute to functional specialization and individual differences in behavior across species. Among species with gyrencephalic brains, gyral crown height, which measures a key aspect of the morphology of cortical folding, may represent an anatomical characteristic that importantly shapes neural function. Nevertheless, little is known about the relationship between functional selectivity and gyral crowns--especially in clinical populations. Here, we investigated this relationship and found that the size and gyral crown height of the middle, but not posterior, face-selective region on the fusiform gyrus (FG) was smaller in individuals with developmental prosopagnosia (DPs; N = 22, 68% female, aged 25-62) compared to neurotypical controls (NTs; N = 25, 60% females, aged 21-55), and this difference was related to face perception. Additional analyses replicated the relationship between gyral crowns and face selectivity in 1,053 NTs (55% females, aged 22-36). These results inform theoretical models of face processing while also providing a novel neuroanatomical feature contributing to the cortical infrastructure supporting face processing. Significance StatementUnderstanding how brain structure supports specialized brain functions is a central goal of neuroscience. Here, we identified a role of gyral crown height--an understudied cortical feature--in shaping the cortical infrastructure underlying face processing. By examining face-selective regions of the fusiform gyrus in both neurotypical individuals and those with developmental prosopagnosia, we demonstrate that reduced gyral crown height is associated with diminished face-selective region surface area and impaired face recognition ability. Furthermore, this structural-functional relationship extends to a large neurotypical sample of over 1,000 individuals, highlighting a generalizable link between cortical anatomy and functional specialization. These findings introduce a new neuroanatomical factor to theoretical models of face perception, which could extend to additional neurodevelopmental disorders and other cognitive tasks.

neuroscience↗

bioRxiv2023

A cortical surface template for human neuroscience

Neuroimaging data analysis relies on normalization to standard anatomical templates to resolve macroanatomical differences across brains. Existing human cortical surface templates sample locations unevenly because of distortions introduced by inflation of the folded cortex into a standard shape. Here we present the onavg template, which affords uniform sampling of the cortex. We created the onavg template based on openly-available high-quality structural scans of 1,031 brains--25 times more than existing cortical templates. We optimized the vertex locations based on cortical anatomy, achieving an even distribution. We observed consistently higher multivariate pattern classification accuracies and representational geometry inter-subject correlations based on onavg than on other templates, and onavg only needs 3/4 as much data to achieve the same performance compared to other templates. The optimized sampling also reduces CPU time across algorithms by 1.3%-22.4% due to less variation in the number of vertices in each searchlight.

neuroscience↗

bioRxiv2022

Cross-movie prediction of individualized functional topography

Participant-specific, functionally-defined brain areas are usually mapped with functional localizers and estimated by making contrasts between responses to single categories of input. Naturalistic stimuli engage multiple brain systems in parallel, provide more ecologically plausible estimates of real-world statistics, and are friendly to special populations. The current study shows that cortical functional topographies in individual participants can be estimated with high fidelity from naturalistic stimuli. Importantly, we demonstrate that robust, individualized estimates can be obtained even when participants watched different movies, were scanned with different parameters/scanners, and were sampled from different institutes across the world. Our results create a foundation for future studies that allow researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate high-level cognitive functions across datasets from laboratories worldwide.

neuroscience↗

bioRxiv2022

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

Quantifying how brain functional architecture differs from person to person is a key challenge in human neuroscience. Current individualized models of brain functional organization are based on brain regions and networks, limiting their use in studying fine-grained vertex-level differences. In this work, we present the Individualized Neural Tuning (INT) model, a fine-grained individualized model of brain functional organization. The INT model is designed to have vertex-level granularity, to capture both representational and topographic differences, and to model stimulus-general neural tuning. Through a series of analyses, we demonstrate that (a) our INT model provides a reliable individualized measure of fine-grained brain functional organization, (b) it accurately predicts individualized brain response patterns to new stimuli, and (c) it requires only 10-20 minutes of data for good performance. The high reliability, specificity, precision, and generalizability of our INT model affords new opportunities for building brain-based biomarkers based on naturalistic neuroimaging paradigms.

neuroscience↗

bioRxiv2021

Not so fast: Limited validity of deep convolutional neural networks as in silico models for human naturalistic face processing

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this novel naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully-connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces, but less accurately capture individuation and dynamic features.

neuroscience↗

bioRxiv2020

The Grand Budapest Hotel: an fMRI dataset in response to a socially-rich, naturalistic movie

Naturalistic stimuli evoke strong, consistent, and information-rich patterns of brain activity, and engage large extents of the human brain. They allow researchers to compare highly similar brain responses across subjects, and to study how complex representations are encoded in brain activity. Here, we describe and share a dataset where 25 subjects watched part of the feature film "The Grand Budapest Hotel" by Wes Anderson. The movie has a large cast with many famous actors. Throughout the story, the camera shots highlight faces and expressions, which are fundamental to understand the complex narrative of the movie. This movie was chosen to sample brain activity specifically related to social interactions and face processing. This dataset provides researchers with fMRI data that can be used to explore social cognitive processes and face processing, adding to the existing neuroimaging datasets that sample brain activity with naturalistic movies.

neuroscience↗