Searchlight analysis: promise, pitfalls, and potential

The neural mechanisms for the recognition of face identity in humans

Every day we encounter dozens of people, and in order to interact with them appropriately we need to recognize their identity. The face is a crucial source of information to recognize a person’s identity. However, recognizing the identity of a face is challenging because it requires distinguishing between very similar images (e.g., the front views of two different faces) while categorizing very different images (e.g., a front view and a profile) as the same person. Neuroimaging has the whole-brain coverage needed to investigate where representations of face identity are encoded, but it is limited in terms of spatial and temporal resolution. In this article, we review recent neuroimaging research that attempted to investigate the representation of face identity, the challenges it faces, and the proposed solutions, to conclude that given the current state of the evidence the right anterior temporal lobe is the most promising candidate region for the representation of face identity.

Using fMRI to decode true thoughts independent of intention to conceal

Multi-variate pattern analysis (MVPA) applied to BOLD-fMRI has proven successful at decoding complicated fMRI signal patterns associated with a variety of cognitive processes. One cognitive process, not yet investigated, is the mental representation of "Yes/No" thoughts that precede the actual overt response to a binary "Yes/No" question. In this study, we focus on examining: (1) whether spatial patterns of the hemodynamic response carry sufficient information to allow reliable decoding of "Yes/No" thoughts; and (2) whether decoding of "Yes/No" thoughts is independent of the intention to respond honestly or dishonestly. To achieve this goal, we conducted two separate experiments. Experiment 1, collected on a 3T scanner, examined the whole brain to identify regions that carry sufficient information to permit significantly above-chance prediction of "Yes/No" thoughts at the group level. In Experiment 2, collected on a 7T scanner, we focused on the regions identified in Experiment 1 to examine the capability of achieving high decoding accuracy at the single subject level. A set of regions--namely right superior temporal gyrus, left supra-marginal gyrus, and left middle frontal gyrus--exhibited high decoding power. Decoding accuracy for these regions increased with trial averaging. When 18 trials were averaged, the median accuracies were 82.5%, 77.5%, and 79.5%, respectively. When trials were separated according to deceptive intentions (set via experimental cues), and classifiers were trained on honest trials, but tested on trials where subjects were asked to deceive, the median accuracies of these regions still reached 66%, 75%, and 78.5%. These results provide evidence that concealed "Yes/No" thoughts are encoded in the BOLD signal, retaining some level of independence from the subject's intentions to answer honestly or dishonestly. These findings also suggest the theoretical possibility for more efficient brain-computer interfaces where subjects only need to think their answers to communicate.

Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping

Although ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced. The improvement in sensitivity provided by multivariate analysis techniques, as compared with univariate methods, then becomes especially welcome. Information mapping approaches are commonly used, such as the searchlight technique, which take into account the spatially distributed patterns of activation in order to predict stimulus conditions. However, the popular searchlight decoding technique, in particular, has been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. We propose the combination of a non-parametric and permutation-based statistical framework with linear classifiers. We term this new combined method Feature Weight Mapping (FWM). The main goal of the proposed method is to map the specific contribution of each voxel to the classification decision while including a correction for the multiple comparisons problem. Next, we compare this new method to the searchlight approach using a simulation and ultra-high-field 7T experimental data. We found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, FWM was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, global multivariate methods provide a substantial improvement for characterizing structure-function relationships.

Representation of the material properties of objects in the visual cortex of nonhuman primates

Information about the material from which objects are made provide rich and useful clues that enable us to categorize and identify those objects, know their state (e.g., ripeness of fruits), and properly act on them. However, despite its importance, little is known about the neural processes that underlie material perception in nonhuman primates. Here we conducted an fMRI experiment in awake macaque monkeys to explore how information about various real-world materials is represented in the visual areas of monkeys, how these neural representations correlate with perceptual material properties, and how they correspond to those in human visual areas that have been studied previously. Using a machine-learning technique, the representation in each visual area was read out from multivoxel patterns of regional activity elicited in response to images of nine real-world material categories (metal, wood, fur, etc.). The congruence of the neural representations with either a measure of low-level image properties, such as spatial frequency content, or with the visuotactile properties of materials, such as roughness, hardness, and warmness, were tested. We show that monkey V1 shares a common representation with human early visual areas reflecting low-level image properties. By contrast, monkey V4 and the posterior inferior temporal cortex represent the visuotactile properties of material, as in human ventral higher visual areas, although there were some interspecies differences in the representational structures. We suggest that, in monkeys, V4 and the posterior inferior temporal cortex are important stages for constructing information about the material properties of objects from their low-level image features.