Searchlight Multivoxel Pattern Analysis Research Articles

Familiarity Processing through Faces and Names: Insights from Multivoxel Pattern Analysis.

The way our brain processes personal familiarity is still debatable. We used searchlight multivoxel pattern analysis (MVPA) to identify areas where local fMRI patterns could contribute to familiarity detection for both faces and name categories. Significantly, we identified cortical areas in frontal, temporal, cingulate, and insular areas, where it is possible to accurately cross-classify familiar stimuli from one category using a classifier trained with the stimulus from the other (i.e., abstract familiarity) based on local fMRI patterns. We also discovered several areas in the fusiform gyrus, frontal, and temporal regions-primarily lateralized to the right hemisphere-supporting the classification of familiar faces but failing to do so for names. Also, responses to familiar names (compared to unfamiliar names) consistently showed less activation strength than responses to familiar faces (compared to unfamiliar faces). The results evinced a set of abstract familiarity areas (independent of the stimulus type) and regions specifically related only to face familiarity, contributing to recognizing familiar individuals.

Distinct patterns of hippocampal activity associated with color and spatial source memory.

The hippocampus is known to be involved in source memory across a wide variety of stimuli and source types. Thus, source memory activity in the hippocampus is thought to be domain-general such that different types of source information are similarly processed in the hippocampus. However, there is some evidence of domain-specificity for spatial and temporal source information. The current fMRI study aimed to determine whether patterns of activity in the hippocampus differed for two types of visual source information: spatial location and background color. Participants completed three runs of a spatial memory task and three runs of a color memory task. During the study phase, 32 line drawings of common objects and animals were presented to either the left or right of fixation for the spatial memory task or on either a red or green background for the color memory task. During the test phase of both tasks, 48 object word labels were presented in the center of the screen and participants classified the corresponding item as old and previously on the "left"/on a "green" background, old and previously on the "right"/on a "red" background, or "new." Two analysis methods were employed to assess whether hippocampal activity differed between the two source types: a general linear model analysis and a classification-based searchlight multivoxel pattern analysis (MVPA). The searchlight MVPA revealed that activity associated with spatial memory and color memory could be classified with above-chance accuracy in a region of the right anterior hippocampus, and a follow-up analysis revealed that there was a significant effect of memory accuracy. These results indicate that different types of source memory are represented by distinct patterns of activity in the hippocampus.

Representation of semantic typicality in brain activation in healthy adults and individuals with aphasia: A multi-voxel pattern analysis

This study aimed to investigate brain regions that show different activation patterns between semantically typical and atypical items in both healthy adults and individuals with aphasia (PWA). Eighteen neurologically healthy adults and twenty-one PWA participated in an fMRI semantic feature verification task that included typical and atypical stimuli from five different semantic categories. A whole-brain searchlight multi-voxel pattern analysis (MVPA) was conducted to classify brain activation patterns between typical and atypical conditions in each participant group separately. Behavioral responses were faster and more accurate for typical vs. atypical items across both groups. The searchlight MVPA identified two significant clusters in healthy adults: left middle occipital gyrus and right calcarine cortex, but no significant clusters were found in PWA. A follow-up analysis in PWA revealed a significant association between neural classification of semantic typicality in the left middle occipital gyrus and reaction times in the fMRI task. When the typicality effect was examined for each semantic category at the univariate level, significance was identified in the visual cortex for fruits in both groups of participants. These findings suggest that semantic typicality was modulated in the visual cortex in healthy individuals, but to a lesser extent in the same region in PWA.

Cortical Regions Encoding Hardness Perception Modulated by Visual Information Identified by Functional Magnetic Resonance Imaging With Multivoxel Pattern Analysis.

Recent studies have revealed that hardness perception is determined by visual information along with the haptic input. This study investigated the cortical regions involved in hardness perception modulated by visual information using functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA). Twenty-two healthy participants were enrolled. They were required to place their left and right hands at the front and back, respectively, of a mirror attached to a platform placed above them while lying in a magnetic resonance scanner. In conditions SFT, MED, and HRD, one of three polyurethane foam pads of varying hardness (soft, medium, and hard, respectively) was presented to the left hand in a given trial, while only the medium pad was presented to the right hand in all trials. MED was defined as the control condition, because the visual and haptic information was congruent. During the scan, the participants were required to push the pad with the both hands while observing the reflection of the left hand and estimate the hardness of the pad perceived by the right (hidden) hand based on magnitude estimation. Behavioral results showed that the perceived hardness was significantly biased toward softer or harder in >73% of the trials in conditions SFT and HRD; we designated these trials as visually modulated (SFTvm and HRDvm, respectively). The accuracy map was calculated individually for each of the pair-wise comparisons of (SFTvm vs. MED), (HRDvm vs. MED), and (SFTvm vs. HRDvm) by a searchlight MVPA, and the cortical regions encoding the perceived hardness with visual modulation were identified by conjunction of the three accuracy maps in group analysis. The cluster was observed in the right sensory motor cortex, left anterior intraparietal sulcus (aIPS), bilateral parietal operculum (PO), and occipito-temporal cortex (OTC). Together with previous findings on such cortical regions, we conclude that the visual information of finger movements processed in the OTC may be integrated with haptic input in the left aIPS, and the subjective hardness perceived by the right hand with visual modulation may be processed in the cortical network between the left PO and aIPS.

Awake fMRI Reveals Brain Regions for Novel Word Detection in Dogs.

How do dogs understand human words? At a basic level, understanding would require the discrimination of words from non-words. To determine the mechanisms of such a discrimination, we trained 12 dogs to retrieve two objects based on object names, then probed the neural basis for these auditory discriminations using awake-fMRI. We compared the neural response to these trained words relative to “oddball” pseudowords the dogs had not heard before. Consistent with novelty detection, we found greater activation for pseudowords relative to trained words bilaterally in the parietotemporal cortex. To probe the neural basis for representations of trained words, searchlight multivoxel pattern analysis (MVPA) revealed that a subset of dogs had clusters of informative voxels that discriminated between the two trained words. These clusters included the left temporal cortex and amygdala, left caudate nucleus, and thalamus. These results demonstrate that dogs’ processing of human words utilizes basic processes like novelty detection, and for some dogs, may also include auditory and hedonic representations.

Decoding auditory spatial and emotional information encoding using multivariate versus univariate techniques

Emotion can have diverse effects on behaviour and perception, modulating function in some circumstances, and sometimes having little effect. Recently, it was identified that part of the heterogeneity of emotional effects could be due to a dissociable representation of emotion in dual pathway models of sensory processing. Our previous fMRI experiment using traditional univariate analyses showed that emotion modulated processing in the auditory ‘what’ but not ‘where’ processing pathway. The current study aims to further investigate this dissociation using a more recently emerging multi-voxel pattern analysis searchlight approach. While undergoing fMRI, participants localized sounds of varying emotional content. A searchlight multi-voxel pattern analysis was conducted to identify activity patterns predictive of sound location and/or emotion. Relative to the prior univariate analysis, MVPA indicated larger overlapping spatial and emotional representations of sound within early secondary regions associated with auditory localization. However, consistent with the univariate analysis, these two dimensions were increasingly segregated in late secondary and tertiary regions of the auditory processing streams. These results, while complimentary to our original univariate analyses, highlight the utility of multiple analytic approaches for neuroimaging, particularly for neural processes with known representations dependent on population coding.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception.

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness.

"Hearing faces and seeing voices": Amodal coding of person identity in the human brain.

Recognizing familiar individuals is achieved by the brain by combining cues from several sensory modalities, including the face of a person and her voice. Here we used functional magnetic resonance (fMRI) and a whole-brain, searchlight multi-voxel pattern analysis (MVPA) to search for areas in which local fMRI patterns could result in identity classification as a function of sensory modality. We found several areas supporting face or voice stimulus classification based on fMRI responses, consistent with previous reports; the classification maps overlapped across modalities in a single area of right posterior superior temporal sulcus (pSTS). Remarkably, we also found several cortical areas, mostly located along the middle temporal gyrus, in which local fMRI patterns resulted in identity “cross-classification”: vocal identity could be classified based on fMRI responses to the faces, or the reverse, or both. These findings are suggestive of a series of cortical identity representations increasingly abstracted from the input modality.

Decoding pressure stimulation locations on the fingers from human neural activation patterns.

In this functional MRI study, we investigated how the human brain activity represents tactile location information evoked by pressure stimulation on fingers. Using the searchlight multivoxel pattern analysis, we looked for local activity patterns that could be decoded into one of four stimulated finger locations. The supramarginal gyrus (SMG) and the thalamus were found to contain distinct multivoxel patterns corresponding to individual stimulated locations. In contrast, the univariate general linear model analysis contrasting stimulation against resting phases for each finger identified activations mainly in the primary somatosensory cortex (S1), but not in SMG or in thalamus. Our results indicate that S1 might be involved in the detection of the presence of pressure stimuli, whereas the SMG and the thalamus might play a role in identifying which finger is stimulated. This finding may provide additional evidence for hierarchical information processing in the human somatosensory areas.

Neural Categorization of Vibrotactile Frequency in Flutter and Vibration Stimulations: An fMRI Study.

As the use of wearable haptic devices with vibrating alert features is commonplace, an understanding of the perceptual categorization of vibrotactile frequencies has become important. This understanding can be substantially enhanced by unveiling how neural activity represents vibrotactile frequency information. Using functional magnetic resonance imaging (fMRI), this study investigated categorical clustering patterns of the frequency-dependent neural activity evoked by vibrotactile stimuli with gradually changing frequencies from 20 to 200 Hz. First, a searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions exhibiting neural activities associated with frequency information. We found that the contralateral postcentral gyrus (S1) and the supramarginal gyrus (SMG) carried frequency-dependent information. Next, we applied multidimensional scaling (MDS) to find low-dimensional neural representations of different frequencies obtained from the multi-voxel activity patterns within these regions. The clustering analysis on the MDS results showed that neural activity patterns of 20-100 Hz and 120-200 Hz were divided into two distinct groups. Interestingly, this neural grouping conformed to the perceptual frequency categories found in the previous behavioral studies. Our findings therefore suggest that neural activity patterns in the somatosensory cortical regions may provide a neural basis for the perceptual categorization of vibrotactile frequency.

Decoding Accuracy in Supplementary Motor Cortex Correlates with Perceptual Sensitivity to Tactile Roughness.

Perceptual sensitivity to tactile roughness varies across individuals for the same degree of roughness. A number of neurophysiological studies have investigated the neural substrates of tactile roughness perception, but the neural processing underlying the strong individual differences in perceptual roughness sensitivity remains unknown. In this study, we explored the human brain activation patterns associated with the behavioral discriminability of surface texture roughness using functional magnetic resonance imaging (fMRI). First, a whole-brain searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions from which we could decode roughness information. The searchlight MVPA revealed four brain regions showing significant decoding results: the supplementary motor area (SMA), contralateral postcentral gyrus (S1), and superior portion of the bilateral temporal pole (STP). Next, we evaluated the behavioral roughness discrimination sensitivity of each individual using the just-noticeable difference (JND) and correlated this with the decoding accuracy in each of the four regions. We found that only the SMA showed a significant correlation between neuronal decoding accuracy and JND across individuals; Participants with a smaller JND (i.e., better discrimination ability) exhibited higher decoding accuracy from their voxel response patterns in the SMA. Our findings suggest that multivariate voxel response patterns presented in the SMA represent individual perceptual sensitivity to tactile roughness and people with greater perceptual sensitivity to tactile roughness are likely to have more distinct neural representations of different roughness levels in their SMA.

Distributed functions of detection and discrimination of vibrotactile stimuli in the hierarchical human somatosensory system.

According to the hierarchical view of human somatosensory network, somatic sensory information is relayed from the thalamus to primary somatosensory cortex (S1), and then distributed to adjacent cortical regions to perform further perceptual and cognitive functions. Although a number of neuroimaging studies have examined neuronal activity correlated with tactile stimuli, comparatively less attention has been devoted toward understanding how vibrotactile stimulus information is processed in the hierarchical somatosensory cortical network. To explore the hierarchical perspective of tactile information processing, we studied two cases: (a) discrimination between the locations of finger stimulation; and (b) detection of stimulation against no stimulation on individual fingers, using both standard general linear model (GLM) and searchlight multi-voxel pattern analysis (MVPA) techniques. These two cases were studied on the same data set resulting from a passive vibrotactile stimulation experiment. Our results showed that vibrotactile stimulus locations on fingers could be discriminated from measurements of human functional magnetic resonance imaging (fMRI). In particular, it was in case (a) we observed activity in contralateral posterior parietal cortex (PPC) and supramarginal gyrus (SMG) but not in S1, while in case; (b) we found significant cortical activations in S1 but not in PPC and SMG. These discrepant observations suggest the functional specialization with regard to vibrotactile stimulus locations, especially, the hierarchical information processing in the human somatosensory cortical areas. Our findings moreover support the general understanding that S1 is the main sensory receptive area for the sense of touch, and adjacent cortical regions (i.e., PPC and SMG) are in charge of a higher level of processing and may thus contribute most for the successful classification between stimulated finger locations.