Cognitive Neuroscience Society Meeting 2008 Symposium

Symposium at the Cognitive Neuroscience Society Meeting, 2008.

Hyatt Regency, San Francisco.
Sunday, April 13, 2008, 10:00am - 12:00pm.

Symposium title: Pattern-based fMRI analyses as a route to revealing neural representations

Chair: Rajeev Raizada
Speakers: Jim Haxby, Nikolaus Kriegeskorte, Rajeev Raizada, Geoff Boynton.

Symposium theme: Within any active brain region, many neural representations are intermingled. Because these representations are spatially colocalised, they may elicit the same levels of local average activation, with the result that neuroimaging studies have difficulty telling them apart. Recent studies analysing multi-voxel spatial patterns of fMRI activity are starting to provide new methods for accessing such neural representations, and for relating them to behaviour. This symposium presents examples of such research, from diverse cognitive domains. Jim Haxby will describe how pattern-based analyses reveal distributed representations of objects in visual cortex. Drawing parallels between human and monkey studies, Nikolaus Kriegeskorte will show how information-based fMRI can reveal the structure of categorical representations of faces and objects in inferotemporal cortex. In the domain of speech perception, Rajeev Raizada will show how the distinctness of phonetic representations in the brain can predict people's ability to hear non-native speech contrasts. Moving beyond stimulus-driven neural responses, Geoff Boynton will describe how feature-based attentional signals can be decoded from distributed cortical activity.

Jim Haxby: Distributed neural representation of faces and objects in ventral temporal cortex
Functional brain imaging has revealed a complex, macroscopic organization in the functional architecture of the ventral object vision pathway. Numerous studies have found regions of ventral temporal cortex that consistently demonstrate category-related response preferences, most notably a region that responds maximally during face perception, the fusiform face area (FFA). Faces and numerous other object categories, however, also evoke distinct patterns of response across wider expanses of ventral temporal cortex, including distinct patterns of response in cortical regions that respond submaximally to the category being viewed, suggesting that the representations of faces and other objects extend beyond the regions defined by category preference. Methods for analyzing these patterns of response, which we call multi-voxel pattern analysis, represent a major departure from previous, standard methods for analyzing functional neuroimaging data. Whereas previous methods were designed to find clusters of voxels with similar response properties, topographic pattern analysis is designed to detect reliable patterns of differences among the responses of voxels. The information-carrying capacity of submaximal responses suggests that these patterns may reflect spatially distributed population responses in which both strong and weak responses play an integral role in the representation of complex percepts. Furthermore, the similarity of patterns of response to visual stimuli is correlated with psychological similarity, suggesting that these methods now allow us to use fMRI to investigate how neural representations of visual stimuli are structured.

Nikolaus Kriegeskorte: Exploiting hi-res fMRI and relating measurement modalities with representational similarity analysis
High-resolution functional magnetic resonance imaging (hi-res fMRI) promises to help bridge the gap of spatial scales between human low-resolution neuroimaging and animal invasive electrophysiology. I will discuss how the fine-scale neuronal-pattern information present in hi-res fMRI data can be exploited for neuroscientific insight by means of multivariate analysis. In particular, I will focus on the novel approach of "representational similarity analysis", which allows us (1) to combine evidence across brain space and experimental conditions to sensitively detect neuronal pattern information and (2) to relate results (a) between different modalities of brain-activity measurement, (b) between different species, and (c) between brain-activity data and computational models of brain information processing. I will illustrate this approach with a study combining human and monkey data from hi-res fMRI and single-cell recordings, respectively. We investigated response patterns elicited by the same 92 photographs of isolated natural objects in inferotemporal (IT) cortex of both species. Within each species, we compute a matrix of response-pattern similarities (one similarity value for each pair of images). We find a striking match of the resulting similarity matrices for man and monkey. This finding suggests very similar categorical IT representations and provides some hope that data from single-cell recording and fMRI, for all their differences, may consistently reveal neuronal representations when subjected to massively multivariate analyses of response-pattern information.

Rajeev Raizada: Predicting individual differences in speech perception using pattern-based fMRI analysis of phonemic representations
The brain's ability to discriminate stimuli depends on how fine-grained its stimulus representations are. This representational granularity can vary across individuals, as a function of factors such as sensory environment and learning history. A key goal of cognitive neuroscience has been to relate the properties of such representations in individuals' brains to their levels of behavioural performance. However, because the neural representations of different but related stimuli are typically colocalised within the same brain area, their distinctness from each other has been difficult for fMRI to measure. This problem has been overcome in low-level sensory cortices, where the representational grain can be calculated from well-defined spatiotopic maps, or from direct mappings between stimulus-energy and levels of neural activation. However, for all but the simplest stimuli, no such mappings are available. For example, different phonemes such as /ra/ and /la/ activate the same areas of cortex, but there is no known "phonotopic map" that might allow the distinctness of the evoked neural representations to be measured. I will describe how, by analysing the multi-voxel spatial fMRI patterns elicited by these stimuli in English and Japanese speakers, the statistical separability of such neural representations can be directly quantified. Moreover, in right primary auditory cortex, the separability of these fMRI patterns strongly predicted the degree to which subjects could behaviourally discriminate the stimuli that gave rise to them. This opens up a new method, which may have broad applicability, for relating neural representations in the human brain to levels of behavioural performance, and also reveals a hitherto unknown role played by right auditory cortex in processing speech.

Geoff Boynton: Pattern-based decoding of feature-specific visual attention
When faced with a crowded visual scene, observers must selectively attend to behaviorally relevant objects to avoid sensory overload. Often this selection process is guided by prior knowledge of a target-defining feature (e.g., the color red when looking for an apple), which enhances the firing rate of visual neurons that are selective for the attended feature. Here, we used functional magnetic resonance imaging and a pattern classification algorithm to predict the attentional state of human observers as they monitored a visual feature (one of two directions of motion). We find that feature-specific attention effects spread across the visual field - even to regions of the scene that do not contain a stimulus. This spread of feature-based attention to empty regions of space may facilitate the perception of behaviorally relevant stimuli by increasing sensitivity to attended features at all locations in the visual field.