Abstract:Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, c… Show more
“…To account for activation differences between runs, the mean activation across all blocks was subtracted from each voxel's values, separately for each run. Decoding analyses were performed using CoSMoMVPA (Oosterhof, Connolly, & Haxby, ), and were carried out separately for each ROI and participant. We used data from four runs to train linear discriminant analysis (LDA) classifiers to discriminate multi‐voxel response patterns (i.e., patterns of voxel activations across all voxels of an ROI) for two conditions (e.g., spatially intact versus spatially jumbled scenes).…”
Section: Methodsmentioning
confidence: 99%
“…EEG decoding was performed separately for each time point (i.e., every 5 ms) from –200 ms to 800 ms relative to stimulus onset, using CoSMoMVPA (Oosterhof et al, ). We used data from all‐but‐one trials for two conditions to train LDA classifiers to discriminate topographical response patterns (i.e., patterns across all electrodes) for two conditions (e.g., spatially intact versus spatially jumbled scenes).…”
Natural scenes are inherently structured, with meaningful objects appearing in predictable locations. Human vision is tuned to this structure: When scene structure is purposefully jumbled, perception is strongly impaired. Here, we tested how such perceptual effects are reflected in neural sensitivity to scene structure. During separate fMRI and EEG experiments, participants passively viewed scenes whose spatial structure (i.e., the position of scene parts) and categorical structure (i.e., the content of scene parts) could be intact or jumbled. Using multivariate decoding, we show that spatial (but not categorical) scene structure profoundly impacts on cortical processing: Scene‐selective responses in occipital and parahippocampal cortices (fMRI) and after 255 ms (EEG) accurately differentiated between spatially intact and jumbled scenes. Importantly, this differentiation was more pronounced for upright than for inverted scenes, indicating genuine sensitivity to spatial structure rather than sensitivity to low‐level attributes. Our findings suggest that visual scene analysis is tightly linked to the spatial structure of our natural environments. This link between cortical processing and scene structure may be crucial for rapidly parsing naturalistic visual inputs.
“…To account for activation differences between runs, the mean activation across all blocks was subtracted from each voxel's values, separately for each run. Decoding analyses were performed using CoSMoMVPA (Oosterhof, Connolly, & Haxby, ), and were carried out separately for each ROI and participant. We used data from four runs to train linear discriminant analysis (LDA) classifiers to discriminate multi‐voxel response patterns (i.e., patterns of voxel activations across all voxels of an ROI) for two conditions (e.g., spatially intact versus spatially jumbled scenes).…”
Section: Methodsmentioning
confidence: 99%
“…EEG decoding was performed separately for each time point (i.e., every 5 ms) from –200 ms to 800 ms relative to stimulus onset, using CoSMoMVPA (Oosterhof et al, ). We used data from all‐but‐one trials for two conditions to train LDA classifiers to discriminate topographical response patterns (i.e., patterns across all electrodes) for two conditions (e.g., spatially intact versus spatially jumbled scenes).…”
Natural scenes are inherently structured, with meaningful objects appearing in predictable locations. Human vision is tuned to this structure: When scene structure is purposefully jumbled, perception is strongly impaired. Here, we tested how such perceptual effects are reflected in neural sensitivity to scene structure. During separate fMRI and EEG experiments, participants passively viewed scenes whose spatial structure (i.e., the position of scene parts) and categorical structure (i.e., the content of scene parts) could be intact or jumbled. Using multivariate decoding, we show that spatial (but not categorical) scene structure profoundly impacts on cortical processing: Scene‐selective responses in occipital and parahippocampal cortices (fMRI) and after 255 ms (EEG) accurately differentiated between spatially intact and jumbled scenes. Importantly, this differentiation was more pronounced for upright than for inverted scenes, indicating genuine sensitivity to spatial structure rather than sensitivity to low‐level attributes. Our findings suggest that visual scene analysis is tightly linked to the spatial structure of our natural environments. This link between cortical processing and scene structure may be crucial for rapidly parsing naturalistic visual inputs.
“…3 e , f ) [36,40] or to each other (figure 3 g ) [30,58]. Importantly, excellent toolboxes that ease the application of RSA in different programming environments are readily available [59,60]. Figure 3.RSA as a quantitative framework for combining models and data from different neuroimaging techniques.…”
Section: A Tripartite Approach To Tackle Current Methodsological Challmentioning
In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research.This article is part of the themed issue ‘Auditory and visual scene analysis’.
“…To calculate the z value associated with each beta value (reflecting the fit of the behavioral matrix with the neural matrix for that searchlight), we used the Cosmo Monte Carlo Cluster Stat function with multiple comparison correction within the CoSMo toolbox (see Oosterhof, Connolly, & Haxby, 2016). This function was run separately for each ROI.…”
Section: Methodsmentioning
confidence: 99%
“…4 Next, null TFCE distributions are generated by randomly flipping the signs of the observed beta values and performing t tests on each of 10,000 permutations. Finally, a final z map is derived by comparing, for each searchlight center, the number of times the observed TFCE was smaller than the maximum TFCE in the null maps and dividing this by the number of iterations (therefore correcting for all comparisons within an ROI; see Oosterhof et al, 2016). …”
Our use of tools is situated in different contexts. Prior evidence suggests that diverse regions within the ventral and dorsal streams represent information supporting common tool use. However, given the flexibility of object concepts, these regions may be tuned to different types of information when generating novel or uncommon uses of tools. To investigate this, we collected fMRI data from participants who reported common or uncommon tool uses in response to visually presented familiar objects. We performed a pattern dissimilarity analysis in which we correlated cortical patterns with behavioral measures of visual, action, and category information. The results showed that evoked cortical patterns within the dorsal tool use network reflected action and visual information to a greater extent in the uncommon use group, whereas evoked neural patterns within the ventral tool use network reflected categorical information more strongly in the common use group. These results reveal the flexibility of cortical representations of tool use and the situated nature of cortical representations more generally.
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