A common, high-dimensional model of the representational space in human ventral temporal cortex

By James V. Haxby, J. Swaroop Guntupalli, Andrew C. Connolly, Yaroslav O. Halchenko, Bryan R. Conroy, Maria Ida Gobbini, Michael Hanke, Peter J. Ramadge. 2011

709 citations recorded by the source corpus.

Summary We present a high-dimensional model of the representational space in human ventral temporal (VT) cortex in which dimensions are response-tuning functions that are common across individuals and patterns of response are modeled as weighted sums of basis patterns associated with these response-tunings. We map response pattern vectors, measured with fMRI, from individual subjects’ voxel spaces into this common model space using a new method, ‘hyperalignment’. Hyperalignment parameters based on responses during one experiment – movie-viewing – identified 35 common response-tuning functions that captured fine-grained distinctions among a wide range of stimuli in the movie and in two category perception experiments. Between-subject classification (BSC, multivariate pattern classification based on other subjects’ data) of response pattern vectors in common model space greatly exceeded BSC of anatomically-aligned responses and matched within-subject classification. Results indicate that population codes for complex visual stimuli in VT cortex are based on response-tuning functions that are common across individuals.

Subfields: Cognitive Neuroscience

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Citations in the corpus

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA (2019, background): “One of the key problems in the field is how to extract a shared response from all subjects despite high inter-subject variability and complex nonlinear mappings between latent source and observation [7, 16].”
Groupwise whole-brain parcellation from resting-state fMRI data for network node identification (2013, positive): “The matrix Rs is a generalized rotation matrix in this high-dimensional space (similar to the hyperalignment approach of Haxby et al. (2011)).”
Cross‐validation failure: Small sample sizes lead to large error bars (2017, background): “This requires ﬁne-grain matching of subjects’ anatomy and function, yet it bears the promise of more general representations of cognition (Haxby et al., 2011).”
NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data (2014, neutral): “…no ‘trace’ left after this type of learning has taken place, to help understand complex brain processes from data. fMRI data is a dynamic data that consists of a large number of voxels, measured over time, to represent a subject’s brain activity (e.g. Hanke et al., 2009; Haxby et al., 2011).”
Data-Efficient Mutual Information Neural Estimator (2019, methodology): “We use between-subject time-segment classification (BSC) for evaluation [25, 22].”
CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave (2016, background): “CoSMoMVPA provides functions to generate a variety of partitioning schemes including those for crossmodal (Oosterhof, Wiggett, Diedrichsen, tipper, & Downing, 2010, Oosterhof, tipper, & Downing, 2012) and crossparticipant (Clithero, Smith, Carter, & Huettel, 2011; Haxby et al., 2011) decoding (Figur”
Building a Science of Individual Differences from fMRI. (2016, methodology): “Accordingly, there has been interest in using RSFC to define functional parcels or ROIs in single subjects’ brains.”
Multivariate pattern analysis of fMRI: The early beginnings (2012, background): “The relevance and utility of these methods were demonstrated through collaborations with colleagues who reanalyzed my data using neural net classifiers (Hanson et al., 2004) and linear discriminant analysis (LDA) (O'Toole et al., 2005) and through subsequent papers from groups that analyzed similar ”
The Human Connectome Project’s Neuroimaging Approach (2016, background): “…surface registration as a starting point, even if they choose to make final comparisons across subjects using other means; for example, hyper-alignment 79 Figure 1 Combined representation of cortical surface vertices and subcortical voxels in the CIFTI grayordinates standard space.”
Confounds in multivariate pattern analysis: Theory and rule representation case study (2013, neutral): “For example, Haynes et al. (2007) reported that (the intention to perform) addition versus subtraction is represented in medial frontal cortex.”
Dimensionality of object representations in monkey inferotemporal cortex (2014, methodology): “P A A uthor M anuscript recordings, though there has been previous work based on human psychophysics (Meytlis & Sirovich, 2007; Sirovich & Meytlis, 2009) and fMRI (Haxby et al., 2011).”
Prediction of psychosis across protocols and risk cohorts using automated language analysis (2018, methodology): “When this UCLA machine-learning classiﬁer was applied to the original NYC speech data, after Procrustean transformation 20,21,24 , it signiﬁcantly discriminated CHR with respect to psychosis onset (p 0.05 upon label randomization), with a true negative ratio of 0.82 (24/29) and a true positive…”
Object-Specific Semantic Coding in Human Perirhinal Cortex (2014, background): “One promising approach to untangling potential representational principles in posterior VTC is to represent activation patterns in a high-dimensional space, that may be used to test whether categorical effects are emergent from a combination of noncategory factors (Op de Beeck et al., 2008; Haxby et”
Multimodal Surface Matching with Higher-Order Smoothness Constraints☆ (2017, background): “One avenue may be to explore combining hyper-alignment (Langs et al., 2010; Haxby et al., 2011), or graph matching (Ktena et al.”