Biology subjects

Feilong, M.

Publications and source records attributed to Feilong, M..

14 recordsLinked to original sources

bioRxiv2026

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

Subcortical structures are central to cognition, emotion, and social processing, and their dysfunction underlies a wide range of neurological and psychiatric conditions. Yet subcortical fine-scale functional organization has remained largely inaccessible to group-level fMRI analysis: nuclei are small and low-SNR, and their functional topography varies idiosyncratically across individuals in ways that anatomical alignment cannot resolve. This idiosyncrasy carries real stakes. For example, deep brain stimulation targets with more variable functional connectivity across individuals show correspondingly less consistent clinical outcomes. Here we show that this seemingly idiosyncratic subcortical organization in fact reflects shared, fine-grained structure that can be recovered by modeling each individual's connectivity in a common functional space (connectivity hyperalignment, CHA), rather than relying on anatomical correspondence. Applying CHA to fMRI data collected during naturalistic movie viewing, we found that aligning subcortical voxels by their cortical connectivity, rather than their anatomical location, sharply increased cross-subject correspondence in both stimulus-evoked response and connectivity profiles. Critically, this alignment sharpened rather than blurred local structure: neighboring voxels became more functionally distinct from one another, and cortical connectivity targets became substantially more decodable across subjects. These results indicate that human subcortical nuclei possess a common fine-scale functional architecture that is masked by anatomical variability but recoverable at the individual level. This reframes a central methodological constraint in subcortical neuroimaging: rather than trading spatial resolution for signal quality via smoothing, group-level analyses can achieve voxel-level precision by aligning function rather than anatomy, opening a path to studying cortical-subcortical circuit organization in greater detail.

neuroscience↗

bioRxiv2026

Brain Mechanisms of Lexicality in Bilingual Emotion Processing

Understanding how native speakers and second language (L2) learners process language differently is fundamental to theories of language acquisition, comprehension, and use. A central debate in this literature concerns the inconsistent findings in emotional prosody processing between native speakers and L2 learners, partly attributable to stimulus lexicality: some studies use real words, whereas others use pseudowords. To resolve this discrepancy, we measured fMRI responses in 15 native speakers and 15 L2 learners while they listened to Mandarin real-word and pseudoword stimuli. Both groups engaged in overlapping auditory, cognitive, and affective regions, including the auditory cortex, inferior frontal cortex, superior temporal cortex, and the temporoparietal junction. However, the two groups showed distinct responses depending on lexicality: native speakers showed stronger recruitment of temporal regions for pseudowords, whereas L2 learners showed greater frontoparietal activation for real words. These patterns were consistent across positive and negative prosody conditions. Furthermore, among L2 learners, higher proficiency was associated with greater frontal activation for real words, but not pseudowords. Together, these findings demonstrate that the differences between native speakers and L2 learners depend critically on stimulus lexicality, highlighting the role of lexicality in bilingual emotion processing.

neuroscience↗

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

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

Boosting Hyperalignment Performance with Age-specific Templates

Hyperalignment aligns individual brain activity and functional connectivity patterns to a common, high-dimensional model space, resolving idiosyncrasies in functional-anatomical correspondence and revealing shared information encoded in fine-grained spatial patterns. Given that the brain undergoes significant developmental and functional changes over the lifespan, it is likely that certain features in brain functional organization are more prominent in certain age groups than others. In this study, we examined whether age-specific functional templates, as compared to a canonical template, could enhance alignment accuracy across diverse age groups. We used the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset (18 to 87 yo) to build age-specific templates and tested their performance for analyzing data in young and old brains in both the Cam-CAN dataset and the Dallas Lifespan Brain Study (DLBS) dataset (20 to 90 yo). We found the congruent age-specific template outperforms the incongruent template for various analyses, including inter-subject correlation of hyperaligned connectivity profiles and predicting individualized connectomes and brain responses to the movie using the template. The results are consistent across both datasets. This work enhances our understanding of age-related differences in brain function, highlights the benefits of creating age-specific templates to refine hyperalignment model performance, and may contribute to the development of age-sensitive diagnostic tools and interventions for neurological disorders.

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.

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bioRxiv2022

The LEGO theory of the developing functional connectome

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine-scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9 - 10 years; n = 1,115 individuals, both sexes, 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition. Significance statementYears of human magnetic resonance imaging (MRI) research demonstrate that individual variability in resting-state functional connectivity relates to genetics and cognition. However, the various spatial scales where individual differences in connectivity could occur have yet to be considered in childhood brain- behavior association studies. Here, we use novel machine learning approaches to examine the reliability, heritability, and behavioral relevance of different spatial scales of the resting-state functional connectome during childhood. We show that broad features of the connectome are strongly related to heritability, whereas fine details are more reliable and strongly associated with neurocognitive performance. These data indicate that reliable, heritable, and behaviorally-relevant individual differences exist at dissociable scales of the functional connectome.

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

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

Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals brain data into a common model space while maintaining the geometric relationships between distinct activity patterns. The dimensions of this common model can encode any kind of functional profiles shared across individuals, such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Performing hyperalignment with either response-based or connectivity-based input data derives transformations to project individuals neural data from anatomical space into the common model such that functional information is optimally aligned across brains. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we used three separate data sets collected while participants watched feature films to derive transformations representing both response-based and connectivity-based information with a single algorithm. Our new method, hybrid hyperalignment, aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment, all in one information space. These results suggest that a single common information space could encode both shared cortical response and functional connectivity profiles across individuals.

neuroscience↗

bioRxiv2020

The neural basis of intelligence in fine-grained cortical topographies

Intelligent thought is the product of efficient neural information processing, which is embedded in fine-grained, topographically-organized population responses and supported by fine-grained patterns of connectivity among cortical fields. Previous work on the neural basis of intelligence, however, has focused on coarse-grained features of brain anatomy and function, because cortical topographies are highly idiosyncratic at a finer scale, obscuring individual differences in fine-grained connectivity patterns. We used a computational algorithm, hyperalignment, to resolve these topographic idiosyncrasies, and found that predictions of general intelligence based on fine-grained (vertex-by-vertex) connectivity patterns were markedly stronger than predictions based on coarse-grained (region-by-region) patterns. Intelligence was best predicted by fine-grained connectivity in the default and frontoparietal cortical systems, both of which are associated with self-generated thought. Previous work overlooked fine-grained architecture because existing methods couldnt resolve idiosyncratic topographies, preventing investigation where the keys to the neural basis of intelligence are more likely to be found.

neuroscience↗

bioRxiv2018

Reliable individual differences in fine-grained cortical functional architecture

Fine-grained functional organization of cortex is not well-conserved across individuals. As a result, individual differences in cortical functional architecture are confounded by topographic idiosyncrasies--i.e., differences in functional-anatomical correspondence. In this study, we used hyperalignment to align information encoded in topographically variable patterns to study individual differences in fine-grained cortical functional architecture in a common representational space. We characterized the structure of individual differences using three common functional indices, and assessed the reliability of this structure across independent samples of data in a natural vision paradigm. Hyperalignment markedly improved the reliability of individual differences across all three indices by resolving topographic idiosyncrasies and accommodating information encoded in spatially fine-grained response patterns. Our results demonstrate that substantial individual differences in cortical functional architecture exist at fine spatial scales, but are inaccessible with anatomical normalization alone.

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bioRxiv2018

Modeling semantic encoding in a common neural representational space

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individuals unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individuals fine-grained functional-anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models.

neuroscience↗