ArticleImaging neuroscience (Cambridge, Mass.)2023
The individualized neural tuning model: Precise and generalizable cartography of functional architecture in individual brains.
Article in Imaging neuroscience (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Aligning brains into a shared space improves their alignment with large language models.Nature computational science · 2026Article
- Inter-individual and inter-site neural code conversion without shared stimuli.Nature computational science · 2025Article
- Predicting whole-brain neural dynamics from prefrontal cortex functional near-infrared spectroscopy signal during movie-watching.Social cognitive and affective neuroscience · 2025Article
- Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling.bioRxiv : the preprint server for biology · 2025Article
- A cortical surface template for human neuroscience.Nature methods · 2024Article
- Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Integrating anatomical and functional landmarks for interparticipant alignment of imaging data.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Application of hyperalignment to resting state data in individuals with psychosis reveals systematic changes in functional networks and identifies distinct clinical subgroups.Aperture neuro · 2024Article
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Authors and funding
6 authors.
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Abstract
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) for many benchmarks, 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.