Multiclass fMRI data decoding and visualization using supervised self-organizing maps

Autor: Giancarlo Valente, Elia Formisano, Lars Hausfeld
Přispěvatelé: Cognitive Neuroscience, RS: FPN CN 2
Jazyk: angličtina
Rok vydání: 2014
Předmět:
SELECTION
Multivariate statistics
Computer science
Decoding
computer.software_genre
Pattern Recognition
Automated
User-Computer Interface
Voxel
Cerebral Cortex
Brain Mapping
fMRI
HUMAN BRAIN
Magnetic Resonance Imaging
SUPPORT VECTOR MACHINES
medicine.anatomical_structure
Neurology
DISTRIBUTED PATTERNS
Pattern Recognition
Physiological
Algorithms
Decoding methods
SINGLE-SUBJECT
Self-organizing map
HUMAN AUDITORY-CORTEX
Cognitive Neuroscience
VENTRAL TEMPORAL CORTEX
Machine learning
Sensitivity and Specificity
CLASSIFICATION
Multiclass classification
Imaging
Three-Dimensional
Artificial Intelligence
Image Interpretation
Computer-Assisted
medicine
Humans
Self-organizing maps
business.industry
Reproducibility of Results
Pattern recognition
Visualization
Support vector machine
DRUG DISCOVERY
Visual cortex
Multivariate Analysis
HUMAN VISUAL-CORTEX
Artificial intelligence
Nerve Net
business
computer
Classifier (UML)
Zdroj: Neuroimage, 96, 54-66. Elsevier Science
ISSN: 1095-9572
1053-8119
DOI: 10.1016/j.neuroimage.2014.02.006
Popis: When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches).
Databáze: OpenAIRE
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