Principal Component Regression Predicts Functional Responses across Individuals
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Summary
A novel analysis framework is introduced, where the amount of variance that is fit by a random effects subspace learned on other images is estimated; it is shown that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability.
- Type
- article
- Published
- 2014-09-14
- Cited by
- 7
- References
- 15
- Access
- Open access
- OpenAlex
- https://openalex.org/W17789669
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15241851
Keywords
Principal component analysis, Estimator, Neuroimaging, Regression, Computer science
References
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- Extremely randomized trees
- Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses
- "Where do auditory hallucinations come from?"--a brain morphometry study of schizophrenia patients with inner or outer space hallucinations.
- MIMO Networks: The Effects of Interference
- Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases.
- Anatomical connectivity patterns predict face-selectivity in the fusiform gyrus
- Where do auditory hallucinations come from
- Scikit-learn: Machine Learning in Python
Cited by
- Large-scale functional MRI analysis to accumulate knowledge on brain functions. (Analyse à grande échelle d'IRM fonctionnelle pour accumuler la connaissance sur les fonctions cérébrales)
- Differences in Human Cortical Gene Expression Match the Temporal Properties of Large-Scale Functional Networks
- Finer parcellation reveals detailed correlational structure of resting-state fMRI signals.
- Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping
- Auditory features modelling reveals sound envelope representation in striate cortex
- Brain topography beyond parcellations: Local gradients of functional maps
- From deep brain phenotyping to functional atlasing
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