Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

• Published in: Frontiers in human neuroscience Vol. 10; p. 128
• Main Authors: Mandelkow, Hendrik, de Zwart, Jacco A., Duyn, Jeff H.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 31.03.2016; Frontiers Media S.A
• Subjects: Algorithms; Bayesian analysis; BOLD fMRI; Brain mapping; Brain research; Classification; Cognitive ability; Computational neuroscience; Data analysis; Experiments; Functional magnetic resonance imaging; Hypotheses; LDA; Learning algorithms; movies; multivariate pattern analysis; MVPA; Neuroscience; NMR; Nuclear magnetic resonance; Statistical analysis; Statistics; Visual stimuli; movies; nearest-neighbor; multivariate pattern analysis (MVPA); naturalistic stimuli; linear discriminant analysis (LDA); classification; BOLD fMRI; Gaussian Naïve Bayes (GNB)
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2016.00128

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation.