Time-resolved Multivariate Pattern Analysis Research Articles

Vicarious touch: Overlapping neural patterns between seeing and feeling touch

Simulation theories propose that vicarious touch arises when seeing someone else being touched triggers corresponding representations of being touched. Prior electroencephalography (EEG) findings show that seeing touch modulates both early and late somatosensory responses (measured with or without direct tactile stimulation). Functional Magnetic Resonance Imaging (fMRI) studies have shown that seeing touch increases somatosensory cortical activation. These findings have been taken to suggest that when we see someone being touched, we simulate that touch in our sensory systems. The somatosensory overlap when seeing and feeling touch differs between individuals, potentially underpinning variation in vicarious touch experiences. Increases in amplitude (EEG) or cerebral blood flow response (fMRI), however, are limited in that they cannot test for the information contained in the neural signal: seeing touch may not activate the same information as feeling touch. Here, we use time-resolved multivariate pattern analysis on whole-brain EEG data from people with and without vicarious touch experiences to test whether seen touch evokes overlapping neural representations with the first-hand experience of touch. Participants felt touch to the fingers (tactile trials) or watched carefully matched videos of touch to another person's fingers (visual trials). In both groups, EEG was sufficiently sensitive to allow decoding of touch location (little finger vs. thumb) on tactile trials. However, only in individuals who reported feeling touch when watching videos of touch could a classifier trained on tactile trials distinguish touch location on visual trials. This demonstrates that, for people who experience vicarious touch, there is overlap in the information about touch location held in the neural patterns when seeing and feeling touch. The timecourse of this overlap implies that seeing touch evokes similar representations to later stages of tactile processing. Therefore, while simulation may underlie vicarious tactile sensations, our findings suggest this involves an abstracted representation of directly felt touch.

Using High-Density Electroencephalography to Explore Spatiotemporal Representations of Object Categories in Visual Cortex.

Visual object perception involves neural processes that unfold over time and recruit multiple regions of the brain. Here, we use high-density EEG to investigate the spatiotemporal representations of object categories across the dorsal and ventral pathways. In , human participants were presented with images from two animate object categories (birds and insects) and two inanimate categories (tools and graspable objects). In , participants viewed images of tools and graspable objects from a different stimulus set, one in which a shape confound that often exists between these categories (elongation) was controlled for. To explore the temporal dynamics of object representations, we employed time-resolved multivariate pattern analysis on the EEG time series data. This was performed at the electrode level as well as in source space of two regions of interest: one encompassing the ventral pathway and another encompassing the dorsal pathway. Our results demonstrate shape, exemplar, and category information can be decoded from the EEG signal. Multivariate pattern analysis within source space revealed that both dorsal and ventral pathways contain information pertaining to shape, inanimate object categories, and animate object categories. Of particular interest, we note striking similarities obtained in both ventral stream and dorsal stream regions of interest. These findings provide insight into the spatio-temporal dynamics of object representation and contribute to a growing literature that has begun to redefine the traditional role of the dorsal pathway.

Linking the Brain with Behavior: The Neural Dynamics of Success and Failure in Goal-directed Behavior.

The human brain is extremely flexible and capable of rapidly selecting relevant information in accordance with task goals. Regions of frontoparietal cortex flexibly represent relevant task information such as task rules and stimulus features when participants perform tasks successfully, but less is known about how information processing breaks down when participants make mistakes. This is important for understanding whether and when information coding recorded with neuroimaging is directly meaningful for behavior. Here, we used magnetoencephalography to assess the temporal dynamics of information processing and linked neural responses with goal-directed behavior by analyzing how they changed on behavioral error. Participants performed a difficult stimulus-response task using two stimulus-response mapping rules. We used time-resolved multivariate pattern analysis to characterize the progression of information coding from perceptual information about the stimulus, cue and rule coding, and finally, motor response. Response-aligned analyses revealed a ramping up of perceptual information before a correct response, suggestive of internal evidence accumulation. Strikingly, when participants made a stimulus-related error, and not when they made other types of errors, patterns of activity initially reflected the stimulus presented, but later reversed, and accumulated toward a representation of the "incorrect" stimulus. This suggests that the patterns recorded at later time points reflect an internally generated stimulus representation that was used to make the (incorrect) decision. These results illustrate the orderly and overlapping temporal dynamics of information coding in perceptual decision-making and show a clear link between neural patterns in the late stages of processing and behavior.

Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA has recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. An example implementation of time-resolved MVPA based on linear SVM classification is described, with accompanying code in Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above-chance accuracy for classifying stimuli images. Extensions of the classification analysis are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.

Stimulus Feature-Specific Information Flow Along the Columnar Cortical Microcircuit Revealed by Multivariate Laminar Spiking Analysis.

Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

Previously Reward-Associated Stimuli Capture Spatial Attention in the Absence of Changes in the Corresponding Sensory Representations as Measured with MEG.

Studies of selective attention typically consider the role of task goals or physical salience, but attention can also be captured by previously reward-associated stimuli, even if they are currently task irrelevant. One theory underlying this value-driven attentional capture (VDAC) is that reward-associated stimulus representations undergo plasticity in sensory cortex, thereby automatically capturing attention during early processing. To test this, we used magnetoencephalography to probe whether stimulus location and identity representations in sensory cortex are modulated by reward learning. We furthermore investigated the time course of these neural effects, and their relationship to behavioral VDAC. Male and female human participants first learned stimulus–reward associations. Next, we measured VDAC in a separate task by presenting these stimuli in the absence of reward contingency and probing their effects on the processing of separate target stimuli presented at different time lags. Using time-resolved multivariate pattern analysis, we found that learned value modulated the spatial selection of previously rewarded stimuli in posterior visual and parietal cortex from ∼260 ms after stimulus onset. This value modulation was related to the strength of participants' behavioral VDAC effect and persisted into subsequent target processing. Importantly, learned value did not influence cortical signatures of early processing (i.e., earlier than ∼200 ms); nor did it influence the decodability of stimulus identity. Our results suggest that VDAC is underpinned by learned value signals that modulate spatial selection throughout posterior visual and parietal cortex. We further suggest that VDAC can occur in the absence of changes in early visual processing in cortex.SIGNIFICANCE STATEMENT Attention is our ability to focus on relevant information at the expense of irrelevant information. It can be affected by previously learned but currently irrelevant stimulus–reward associations, a phenomenon termed “value-driven attentional capture” (VDAC). The neural mechanisms underlying VDAC remain unclear. It has been speculated that reward learning induces visual cortical plasticity, which modulates early visual processing to capture attention. Although we find that learned value modulates spatial signals in visual cortical areas, an effect that correlates with VDAC, we find no relevant signatures of changes in early visual processing in cortex.

Decoding Images in the Mind's Eye: The Temporal Dynamics of Visual Imagery.

Mental imagery is the ability to generate images in the mind in the absence of sensory input. Both perceptual visual processing and internally generated imagery engage large, overlapping networks of brain regions. However, it is unclear whether they are characterized by similar temporal dynamics. Recent magnetoencephalography work has shown that object category information was decodable from brain activity during mental imagery, but the timing was delayed relative to perception. The current study builds on these findings, using electroencephalography to investigate the dynamics of mental imagery. Sixteen participants viewed two images of the Sydney Harbour Bridge and two images of Santa Claus. On each trial, they viewed a sequence of the four images and were asked to imagine one of them, which was cued retroactively by its temporal location in the sequence. Time-resolved multivariate pattern analysis was used to decode the viewed and imagined stimuli. Although category and exemplar information was decodable for viewed stimuli, there were no informative patterns of activity during mental imagery. The current findings suggest stimulus complexity, task design and individual differences may influence the ability to successfully decode imagined images. We discuss the implications of these results in the context of prior findings of mental imagery.

No evidence from MVPA for different processes underlying the N300 and N400 incongruity effects in object-scene processing

Attributing meaning to diverse visual input is a core feature of human cognition. Violating environmental expectations (e.g., a toothbrush in the fridge) induces a late event-related negativity of the event-related potential/ERP. This N400 ERP has not only been linked to the semantic processing of language, but also to objects and scenes. Inconsistent object-scene relationships are additionally associated with an earlier negative deflection of the EEG signal between 250 and 350 ms. This N300 is hypothesized to reflect pre-semantic perceptual processes. To investigate whether these two components are truly separable or if the early object-scene integration activity (250–350 ms) shares certain levels of processing with the late neural correlates of meaning processing (350–500 ms), we used time-resolved multivariate pattern analysis (MVPA) where a classifier trained at one time point in a trial (e.g., during the N300 time window) is tested at every other time point (i.e., including the N400 time window). Forty participants were presented with semantic inconsistencies, in which an object was inconsistent with a scene's meaning. Replicating previous findings, our manipulation produced significant N300 and N400 deflections. MVPA revealed above chance decoding performance for classifiers trained during time points of the N300 component and tested during later time points of the N400, and vice versa. This provides no evidence for the activation of two separable neurocognitive processes following the violation of context-dependent predictions in visual scene perception. Our data supports the early appearance of high-level, context-sensitive processes in visual cognition.

Dynamics of neural representations when searching for exemplars and categories of human and non-human faces

Face perception abilities in humans exhibit a marked expertise in distinguishing individual human faces at the expense of individual faces from other species (the other-species effect). In particular, one behavioural effect of such specialization is that human adults search for and find categories of non-human faces faster and more accurately than a specific non-human face, and vice versa for human faces. However, a recent visual search study showed that neural responses (event-related potentials, ERPs) were identical when finding either a non-human or human face. We used time-resolved multivariate pattern analysis of the EEG data from that study to investigate the dynamics of neural representations during a visual search for own-species (human) or other-species (non-human ape) faces, with greater sensitivity than traditional ERP analyses. The location of each target (i.e., right or left) could be decoded from the EEG, with similar accuracy for human and non-human faces. However, the neural patterns associated with searching for an exemplar versus a category target differed for human faces compared to non-human faces: Exemplar representations could be more reliably distinguished from category representations for human than non-human faces. These findings suggest that the other-species effect modulates the nature of representations, but preserves the attentional selection of target items based on these representations.

From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM).

In recent years, time-resolved multivariate pattern analysis (MVPA) has gained much popularity in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. However, MVPA may appear daunting to those who have been applying traditional analyses using event-related potentials (ERPs) or event-related fields (ERFs). To ease this transition, we recently developed the Amsterdam Decoding and Modeling (ADAM) toolbox in MATLAB. ADAM is an entry-level toolbox that allows a direct comparison of ERP/ERF results to MVPA results using any dataset in standard EEGLAB or Fieldtrip format. The toolbox performs and visualizes multiple-comparison corrected group decoding and forward encoding results in a variety of ways, such as classifier performance across time, temporal generalization (time-by-time) matrices of classifier performance, channel tuning functions (CTFs) and topographical maps of (forward-transformed) classifier weights. All analyses can be performed directly on raw data or can be preceded by a time-frequency decomposition of the data in which case the analyses are performed separately on different frequency bands. The figures ADAM produces are publication-ready. In the current manuscript, we provide a cookbook in which we apply a decoding analysis to a publicly available MEG/EEG dataset involving the perception of famous, non-famous and scrambled faces. The manuscript covers the steps involved in single subject analysis and shows how to perform and visualize a subsequent group-level statistical analysis. The processing pipeline covers computation and visualization of group ERPs, ERP difference waves, as well as MVPA decoding results. It ends with a comparison of the differences and similarities between EEG and MEG decoding results. The manuscript has a level of description that allows application of these analyses to any dataset in EEGLAB or Fieldtrip format.

Time-resolved decoding of planned delayed and immediate prehension movements

Different contexts require us either to react immediately, or to delay (or suppress) a planned movement. Previous studies that aimed at decoding movement plans typically dissociated movement preparation and execution by means of delayed-movement paradigms. Here we asked whether these results can be generalized to the planning and execution of immediate movements. To directly compare delayed, non-delayed, and suppressed reaching and grasping movements, we used a slow event-related functional magnetic resonance imaging (fMRI) design. To examine how neural representations evolved throughout movement planning, execution, and suppression, we performed time-resolved multivariate pattern analysis (MVPA). During the planning phase, we were able to decode upcoming reaching and grasping movements in contralateral parietal and premotor areas. During the execution phase, we were able to decode movements in a widespread bilateral network of motor, premotor, and somatosensory areas. Moreover, we obtained significant decoding across delayed and non-delayed movement plans in contralateral primary motor cortex. Our results demonstrate the feasibility of time-resolved MVPA and provide new insights into the dynamics of the prehension network, suggesting early neural representations of movement plans in the primary motor cortex that are shared between delayed and non-delayed contexts.

The Role of the Parietal Cortex in the Representation of Task-Reward Associations.

Rewards obtained from specific behaviors can and do change across time. To adapt to such conditions, humans need to represent and update associations between behaviors and their outcomes. Much previous work focused on how rewards affect the processing of specific tasks. However, abstract associations between multiple potential behaviors and multiple rewards are an important basis for adaptation as well. In this experiment, we directly investigated which brain areas represent associations between multiple tasks and rewards, using time-resolved multivariate pattern analysis of functional magnetic resonance imaging data. Importantly, we were able to dissociate neural signals reflecting task-reward associations from those related to task preparation and reward expectation processes, variables that were often correlated in previous research. We hypothesized that brain regions involved in processing tasks and/or rewards will be involved in processing associations between them. Candidate areas included the dorsal anterior cingulate cortex, which is involved in associating simple actions and rewards, and the parietal cortex, which has been shown to represent task rules and action values. Our results indicate that local spatial activation patterns in the inferior parietal cortex indeed represent task-reward associations. Interestingly, the parietal cortex flexibly changes its content of representation within trials. It first represents task-reward associations, later switching to process tasks and rewards directly. These findings highlight the importance of the inferior parietal cortex in associating behaviors with their outcomes and further show that it can flexibly reconfigure its function within single trials. Significance statement: Rewards obtained from specific behaviors rarely remain constant over time. To adapt to changing conditions, humans need to continuously update and represent the current association between behavior and its outcomes. However, little is known about the neural representation of behavior-outcome associations. Here, we used multivariate pattern analysis of functional magnetic resonance imaging data to investigate the neural correlates of such associations. Our results demonstrate that the parietal cortex plays a central role in representing associations between multiple behaviors and their outcomes. They further highlight the flexibility of the parietal cortex, because we find it to adapt its function to changing task demands within trials on relatively short timescales.