The Representation of Biological Classes in the Human Brain

Connolly, Andrew C.; Guntupalli, J. Swaroop; Gors, Jason; Hanke, Michael; Halchenko, Yaroslav O.; Wu, Yu-Chien; Abdi, Hervé; Haxby, James V.

doi:10.1523/jneurosci.5547-11.2012

Cited by 336 publications

(335 citation statements)

References 43 publications

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“…An ever increasing number of studies have succeeded in detecting patterns of brain activity that reflect the identity or the semantic category of pictures (Haxby et al, 2001;Hanson et al, 2004;Shinkareva et al, 2008;Connolly et al, 2012;Mur et al, 2012;Bruffaerts et al, 2013a), written words Just et al, 2010), written words and pictures (Shinkareva et al, 2011;Simanova et al, 2012;Bruffaerts et al, 2013b;Devereux et al, 2013;Fairhall and Caramazza, 2013), written and spoken words (Akama et al, 2012;Simanova et al, 2012) as well as natural sounds (Simanova et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Left perirhinal cortex codes for similarity in meaning between written words: Comparison with auditory word input

et al. 2015

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This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Neuropsychologia following peer review. The definitive publisher-authenticated version (Liuzzi A.G., Bruffaerts R., Dupont P., Adamczuk K., Peeters R., De Deyne S., Storms G., Vandenberghe R. AbstractLeft perirhinal cortex has been previously implicated in associative coding. According to a recent experiment, the similarity of perirhinal fMRI response patterns to written concrete words is higher for words which are more similar in their meaning. If left perirhinal cortex functions as an amodal semantic hub, one would predict that this semantic similarity effect would extend to the spoken modality. We conducted an event-related fMRI experiment and evaluated whether a same semantic similarity effect could be obtained for spoken as for written words. Twenty healthy subjects performed a property verification task in either the written or the spoken modality. Words corresponded to concrete animate entities for which extensive feature generation was available from more than 1000 subjects. From these feature generation data, a concept-feature matrix was derived which formed the basis of a cosine similarity matrix between the entities reflecting their similarity in meaning (called the "semantic cossimilarity matrix"). Independently, we calculated a cosine similarity matrix between the left perirhinal fMRI activity patterns evoked by the words (called the "fMRI cossimilarity matrix"). Next, the similarity was determined between the semantic cossimilarity matrix and the fMRI cossimilarity matrix. This was done for written and spoken words pooled, for written words only, for spoken words only, as well as for crossmodal pairs. Only for written words did the fMRI cossimilarity matrix correlate with the semantic cossimilarity matrix. Contrary to our prediction, we did not find any such effect for auditory word input nor did we find cross-modal effects in perirhinal cortex between written and auditory words. Our findings situate the contribution of left perirhinal cortex to word processing at the top of the visual processing pathway, rather than at an amodal stage where visual and auditory word processing pathways have already converged.

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Section: Introductionmentioning

confidence: 99%

Left perirhinal cortex codes for similarity in meaning between written words: Comparison with auditory word input

et al. 2015

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“…This data set is analyzed in high-level visual stimuli as the binary predictor, by considering all categories except nonsense photos (scramble) as objects. Please see [5,6,11,14,26] for more information. As the second data set, 'Word and Object Processing' (DS107) includes U = 98 subjects.…”

Section: Experiments 41 Data Setsmentioning

confidence: 99%

Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains

Yousefnezhad

Zhang

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Multivariate Pattern (MVP) classification holds enormous potential for decoding visual stimuli in the human brain by employing task-based fMRI data sets. There is a wide range of challenges in the MVP techniques, i.e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and the cost of brain studies. In overcoming these challenges, this paper proposes a novel model of neural representation, which can automatically detect the active regions for each visual stimulus and then utilize these anatomical regions for visualizing and analyzing the functional activities. Therefore, this model provides an opportunity for neuroscientists to ask this question: what is the effect of a stimulus on each of the detected regions instead of just study the fluctuation of voxels in the manually selected ROIs. Moreover, our method introduces analyzing snapshots of brain image for decreasing sparsity rather than using the whole of fMRI time series. Further, a new Gaussian smoothing method is proposed for removing noise of voxels in the level of ROIs. The proposed method enables us to combine different fMRI data sets for reducing the cost of brain studies. Experimental studies on 4 visual categories (words, consonants, objects and nonsense photos) confirm that the proposed method achieves superior performance to state-of-the-art methods.

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“…Connolly et al [35] have combined RSA, searchlight analysis, and cluster algorithm to explore distributed response to the stimuli images of six animal species without external assumptions. The authors discovered that the largest cluster was formed by voxels in lateral occipital complex (LOC) region, with their RDMs reflecting biological class structure of the stimuli.…”

Section: Representational Connectivity and Similarity Searchlight Anamentioning

confidence: 99%

“…To investigate this question demands across-subject decoding, where visualizing and comparing neural similarity space by MDS and hierarchical clustering analysis are commonly used [23,35]. However, classification by intuitive visualization or averaging alone might not be self-evident at a finer grained scale and not sufficient to judge whether an attempt to decode the stimuli across subjects succeeds or fails.…”

Section: Performing Across-subject Decoding Of Finegrained Neural Repmentioning

confidence: 99%

“…This platform based on similarity measure naturally provides clustering information, along a fine continuum of distance, which enables cross-modal comparison. Connolly et al [35] conducted an fMRI study with a selection of photographs from 6 species and then asked the participants to rate their proximity at a post-scan session. Using behavioral judgments as target similarity structures, the authors applied the searchlight technique to map the relationship between neural similarity and target behavioral similarity throughout the brain.…”

Section: Identifying Semantic Dissimilarity Structure In Human Brain mentioning

confidence: 99%

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Similarity representation of pattern-information fMRI

Xue

Weng

et al. 2013

Chin. Sci. Bull.

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Representational similarity analysis (RSA) is a rapidly developing multivariate platform to investigate the structure of neural activities. Similarity/dissimilarity is the core concept of RSA, realized by the construction of a representational dissimilarity matrix, that addresses the closeness/distance for each pair of research elements (e.g., one minus the correlation between the brain responses to 2 different stimuli) and in turn, constitutes a multivariate pattern as its analytic foundation. This approach is also welcome for its sensitivity in detecting subtle differences of distributed experimental effects in the brain. Importantly, RSA is not only an experimental tool but a promising data-analytical framework that can integrate cross-modal imaging signals, explore brain-behavior link, and verify computational models according to measured neural activities. RSA substantiates its integrative power by relating similarity structure in one domain (e.g., stimulus features) to that in another domain (e.g., neural activities). This review summarizes dissimilarity/similarity definition of RSA, introduces how to derive the dissimilarity structure in neural response pattern, and carry out connectivity analysis based on RSA platform. Several recent advances are highlighted, such as the extraction of across-subjects regularity, cross-validation of brain reactivity in human beings and monkeys, the incorporation of computational models and behavioral profiles into RSA. Voxel receptor field modeling, another promising multivariate tool of pattern elucidation, is presented and compared. The application of RSA is expected to surge and extend in many fields of neuroscience, computation, psychology and medicine. We also discuss the limitations of RSA and some critical questions that need to be addressed in future research.representational similarity analysis, pattern information, condition-rich experiments, decode Citation:Xue S W, Weng X C, He S, et al. Similarity representation of pattern-information fMRI.

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The Representation of Biological Classes in the Human Brain

Cited by 336 publications

References 43 publications

Left perirhinal cortex codes for similarity in meaning between written words: Comparison with auditory word input

Left perirhinal cortex codes for similarity in meaning between written words: Comparison with auditory word input

Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains

Similarity representation of pattern-information fMRI

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