Biology subjects

Haxby, J. V.

Publications and source records attributed to Haxby, J. V..

4 recordsLinked to original sources

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.

neuroscience↗

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↗

bioRxiv2018

Neural responses to naturalistic clips of behaving animals in two different task contexts

Neuroimaging studies of object and action representation often use controlled stimuli and implicitly assume that the relevant neural representational spaces are fixed and context-invariant. Here we present functional MRI data measured while participants freely viewed brief naturalistic video clips of behaving animals in two different task contexts. Participants performed a 1-back category repetition detection task requiring them to attend to either animal taxonomy or animal behavior. The data and analysis code are freely available, and have been curated according to the Brain Imaging Data Structure (BIDS) standard. We thoroughly describe the data, provide quality control metrics, and perform a searchlight classification analysis to demonstrate the potential utility of the data. These data are intended to provide a test bed for investigating how task demands alter the neural representation of complex stimuli and their semantic qualities.

neuroscience↗

bioRxiv2017

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

Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study.\n\nAuthor SummaryResting state fMRI has become a ubiquitous tool for measuring connectivity in normal and diseased brains. Current dominant models of connectivity are based on coarse-scale connectivity among brain regions, ignoring fine-scale structure within those regions. We developed a high-dimensional common model of the human connectome that captures both coarse and fine-scale structure of connectivity shared across brains. We showed that this shared fine-scale structure is related to fine-scale distinctions in representation of information, and our model accounts for substantially more shared variance of connectivity compared to previous models. Our model opens new territory -- shared fine-scale structure, a dominant but mostly unexplored component of the human connectome -- for analysis and study.

neuroscience↗