Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 May 15:12:316.
doi: 10.3389/fnins.2018.00316. eCollection 2018.

Neural Responses to Naturalistic Clips of Behaving Animals in Two Different Task Contexts

Samuel A Nastase  1   2 , Yaroslav O Halchenko  1 , Andrew C Connolly  3 , M Ida Gobbini  1   4 , James V Haxby  1
Affiliations

Neural Responses to Naturalistic Clips of Behaving Animals in Two Different Task Contexts

Samuel A Nastase et al. Front Neurosci. .
No abstract available

Keywords: action understanding; attention; categorization; fMRI; multivariate pattern analysis (MVPA); natural vision; open data.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Experimental design. (A) Schematic of the rapid event-related design for both taxonomy attention and behavior attention task conditions. In the taxonomy attention task, participants were instructed to press a button if they observed a taxonomic category repetition (e.g., two consecutive clips depicting reptiles; upper). In the behavior attention task, participants were instructed to press a button if they observed a behavioral category repetition (e.g., two consecutive clips depicting animals eating; lower). (B) Two example design matrices for predicting hemodynamic responses to the clips over the course of two runs with the taxonomy attention task. In the condition-rich design, each of 80 visually unique stimuli receives a separate predictor (following Kriegeskorte et al., ; upper), while in the category design, the four exemplar clips per taxonomy–behavior condition are collapsed to form 20 category predictors (following Nastase et al., ; lower). Hypothesized neural responses are convolved with a simple hemodynamic response function (Cohen, 1997). In this simple example, nuisance regressors for taxonomy and behavior repetition events, first- through third-order Legendre polynomials, and run constants are appended to each design matrix. Figures were created using Matplotlib (https://matplotlib.org; Hunter, ; RRID:SCR_008624) and seaborn (https://seaborn.pydata.org; Waskom et al., 2016).
Figure 2
Figure 2
Behavioral and taxonomic category cross-classification using surface-based searchlights. To statistically evaluate the searchlight results, we first computed a one-sample t-test against theoretical chance accuracy per searchlight (one-tailed test). We corrected for multiple tests by controlling the false discovery rate (FDR) at q = 0.05 (Benjamini and Hochberg, ; Genovese et al., 2002). The mean classification accuracy across participants is plotted and searchlight maps are thresholded at FDR q = 0.05. (A) Searchlight classification of behavioral categories cross-validated across taxonomic categories while participants attended to animal behavior. Theoretical chance accuracy for four-way behavioral category classification is 0.25. The maximum mean searchlight accuracy for behavioral category classification was 0.56 in left lateral occipitotemporal cortex (inferior occipital gyrus). (B) Searchlight classification of taxonomic categories cross-validated across behavioral categories while participants attended to animal taxonomy. Theoretical chance accuracy for five-way taxonomic category classification is 0.20. The maximum mean searchlight accuracy for taxonomic category classification was 0.36 in right ventral temporal cortex (lateral fusiform gyrus). Although we used a t-test here for simplicity, note that the t-test may yield significant t-values even for near-chance accuracies, and a permutation- or prevalence-based approach may be preferable in some cases (cf. Stelzer et al., ; Allefeld et al., ; Etzel, 2017). Surface vertices on the medial wall were excluded from the analysis and clusters of fewer than ten contiguous significant vertices after thresholding were excluded for visualization purposes. Surface data were visualized using SUMA (Saad et al., ; RRID:SCR_005927) and figures were created using GIMP (https://www.gimp.org; RRID:SCR_003182) and Inkscape (https://inkscape.org; RRID:SCR_014479).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Aguirre G. K. (2007). Continuous carry-over designs for fMRI. Neuroimage 35, 1480–1494. 10.1016/j.neuroimage.2007.02.005 - DOI - PMC - PubMed
    1. Allefeld C., Görgen K., Haynes J. D. (2016). Valid population inference for information-based imaging: from the second-level t-test to prevalence inference. Neuroimage 141, 378–392. 10.1016/j.neuroimage.2016.07.040 - DOI - PubMed
    1. Attneave F. (1950). Dimensions of similarity. Am. J. Psychol. 63, 516–556. 10.2307/1418869 - DOI - PubMed
    1. Bartels A., Zeki S. (2004). Functional brain mapping during free viewing of natural scenes. Hum. Brain Mapp. 21, 75–85. 10.1002/hbm.10153 - DOI - PMC - PubMed
    1. Behzadi Y., Restom K., Liau J., Liu T. T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101. 10.1016/j.neuroimage.2007.04.042 - DOI - PMC - PubMed

LinkOut - more resources