Representational similarity encoding for fMRI: Pattern-based synthesis to predict brain activity using stimulus-model-similarities

Anderson, Andrew; Zinszer, Benjamin D.; Raizada, Rajeev D. S.

doi:10.1016/j.neuroimage.2015.12.035

Cited by 37 publications

(61 citation statements)

References 33 publications

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“…In addition, by relating the patterns of fNIRS activations among stimuli that vary along known dimensions, one can expand MVPA to ask how higher-level stimulus dimensions are decoded by the brain. Now that MVPA has been confirmed as a viable method in infants, future use of Representational Similarity Analysis promises to be a fruitful avenue for investigation, such as implementing methods for more than two conditions, as described by [21] and [35]. …”

Section: Discussionmentioning

confidence: 99%

“…A more interesting distinction is the fact that although univariate activation intensity can only go up or down, multivariate patterns have similarity relations to each other, and therefore induce a structured similarity space (see Representational Similarity Analysis or RSA, [20,21]) Similarity measures can, for example, quantify how much a new observation matches previous observations and thus be used to classify the new observation. These sorts of questions of representational structure simply do not arise in a purely univariate framework and thus provide an opportunity for greater detail rather than a more sensitive contrast between two conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Similarity-based decoding is one such example. This approach uses representational similarity structures (RSA, described above) to classify new observations based on their similarity to other known observations in the similarity structure [21]. A very simple version of an RSA approach is simply using Pearson correlation, and we explore the application of this method to infant fNIRS data.…”

Section: Introductionmentioning

confidence: 99%

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Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS

et al. 2017

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The MRI environment restricts the types of populations and tasks that can be studied by cognitive neuroscientists (e.g., young infants, face-to-face communication). FNIRS is a neuroimaging modality that records the same physiological signal as fMRI but without the constraints of MRI, and with better spatial localization than EEG. However, research in the fNIRS community largely lacks the analytic sophistication of analogous fMRI work, restricting the application of this imaging technology. The current paper presents a method of multivariate pattern analysis for fNIRS that allows the authors to decode the infant mind (a key fNIRS population). Specifically, multivariate pattern analysis (MVPA) employs a correlation-based decoding method where a group model is constructed for all infants except one; both average patterns (i.e., infant-level) and single trial patterns (i.e., trial-level) of activation are decoded. Between subjects decoding is a particularly difficult task, because each infant has their own somewhat idiosyncratic patterns of neural activation. The fact that our method succeeds at across-subject decoding demonstrates the presence of group-level multi-channel regularities across infants. The code for implementing these analyses has been made readily available online to facilitate the quick adoption of this method to advance the methodological tools available to the fNIRS researcher.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS

et al. 2017

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“…In the last decade, multivariate techniques for analyzing distributed patterns of neural activity (e.g., Haxby et al, 2001; Kriegeskorte et al, 2006; 2008; Anderson et al, 2016; Spiridon and Kanwisher, 2002) have grown in prominence as a means to understand how object concepts are coded in cortex. The widespread adoption of multivoxel pattern classification approaches has led to a shift in emphasis from ‘which regions’ of the brain support which ‘types’ or ‘classes’ of concepts, to studying the representational space within those regions.…”

Section: Background and Introduction To The Questionmentioning

confidence: 99%

Arguments about the nature of concepts: Symbols, embodiment, and beyond

Mahon

Hickok

2016

Psychon Bull Rev

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How are the meanings of words, events and objects represented and organized in the brain? This question, perhaps more than any other in the field, probes some of the deepest and most foundational puzzles regarding the structure of the mind and brain. Accordingly it has spawned a field of inquiry that is diverse and multi-disciplinary, has led to the discovery of numerous empirical phenomena and spurred the development of a wide range of theoretical positions. This volume brings together the most recent theoretical developments from the leaders in the field, representing a range of viewpoints on issues of fundamental significance to a theory of meaning representation. Here we introduce the special issue by way of pulling out some key themes that cut across the contributions that form this volume and situating those themes in the broader literature. The core issues around which research on conceptual representation can be organized are: representational format, representational content, the organization of concepts in the brain, and the processing dynamics that govern interactions between the conceptual system and sensorimotor representations. We highlight areas where consensus has formed; for those areas where opinion is divided, we seek to clarify the relation of theory and evidence and to set in relief the bridging assumptions that undergird current discussions.

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“…However, considering multiple channels at once yields more important benefits than simply being more sensitive than univariate analyses. A more interesting distinction is the fact that although univariate activation intensity can only go up or down, multivariate patterns have similarity relations to each other, and therefore induce a structured similarity space (see Anderson, Zinszer, & Raizada, 2015 for a recent examination of similarity-based methods). Similarity measures can, for example, quantify how much a new observation matches previous observations and thus be used to classify the new observation.…”

Section: Introductionmentioning

confidence: 99%

Decoding the Infant Mind: Multichannel Pattern Analysis (MCPA) using fNIRS

Rds

et al. 2016

Preprint

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The MRI environment restricts the types of populations and tasks that can be studied by cognitive neuroscientists (e.g., young infants, face-to-face communication). FNIRS is a neuroimaging modality that records the same physiological signal as fMRI but without the constraints of MRI, and with better spatial localization than EEG. However, research in the fNIRS community largely lacks the analytic sophistication of analogous fMRI work, restricting the application of this imaging technology. The current paper presents a method of multivariate pattern analysis for fNIRS that allows the authors to decode the infant mind (a key fNIRS population). Specifically, multichannel pattern analysis (MCPA) employs a correlation-based decoding method where a group model is constructed for all infants except one; both average patterns (i.e., infant-level) and single trial patterns (i.e., trial-level) of activation are decoded. Between subjects decoding is a particularly difficult task, because each infant has their own somewhat idiosyncratic patterns of neural activation. The fact that our method succeeds at across-subject decoding demonstrates the presence of group-level multichannel regularities across infants. The code for implementing these analyses has been made readily available online to facilitate the quick adoption of this method to advance the methodological tools available to the fNIRS researcher.

show abstract

Representational similarity encoding for fMRI: Pattern-based synthesis to predict brain activity using stimulus-model-similarities

Cited by 37 publications

References 33 publications

Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS

Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS

Arguments about the nature of concepts: Symbols, embodiment, and beyond

Decoding the Infant Mind: Multichannel Pattern Analysis (MCPA) using fNIRS

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