a methodological approach

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research

Communicative signals during joint attention promote neural processes of infants and caregivers

Communicative signals such as eye contact increase infants’ brain activation to visual stimuli and promote joint attention. Our study assessed whether communicative signals during joint attention enhance infant-caregiver dyads’ neural responses to objects, and their neural synchrony. To track mutual attention processes, we applied rhythmic visual stimulation (RVS), presenting images of objects to 12-month-old infants and their mothers (n = 37 dyads), while we recorded dyads’ brain activity (i.e., steady-state visual evoked potentials, SSVEPs) with electroencephalography (EEG) hyperscanning. Within dyads, mothers either communicatively showed the images to their infant or watched the images without communicative engagement. Communicative cues increased infants’ and mothers’ SSVEPs at central-occipital-parietal, and central electrode sites, respectively. Infants showed significantly more gaze behaviour to images during communicative engagement. Dyadic neural synchrony (SSVEP amplitude envelope correlations, AECs) was not modulated by communicative cues. Taken together, maternal communicative cues in joint attention increase infants’ neural responses to objects, and shape mothers’ own attention processes. We show that communicative cues enhance cortical visual processing, thus play an essential role in social learning. Future studies need to elucidate the effect of communicative cues on neural synchrony during joint attention. Finally, our study introduces RVS to study infant-caregiver neural dynamics in social contexts

A multivariate comparison of electroencephalogram and functional magnetic resonance imaging to electrocorticogram using visual object representations in humans

Today, most neurocognitive studies in humans employ the non-invasive neuroimaging techniques functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG). However, how the data provided by fMRI and EEG relate exactly to the underlying neural activity remains incompletely understood. Here, we aimed to understand the relation between EEG and fMRI data at the level of neural population codes using multivariate pattern analysis. In particular, we assessed whether this relation is affected when we change stimuli or introduce identity-preserving variations to them. For this, we recorded EEG and fMRI data separately from 21 healthy participants while participants viewed everyday objects in different viewing conditions, and then related the data to electrocorticogram (ECoG) data recorded for the same stimulus set from epileptic patients. The comparison of EEG and ECoG data showed that object category signals emerge swiftly in the visual system and can be detected by both EEG and ECoG at similar temporal delays after stimulus onset. The correlation between EEG and ECoG was reduced when object representations tolerant to changes in scale and orientation were considered. The comparison of fMRI and ECoG overall revealed a tighter relationship in occipital than in temporal regions, related to differences in fMRI signal-to-noise ratio. Together, our results reveal a complex relationship between fMRI, EEG, and ECoG signals at the level of population codes that critically depends on the time point after stimulus onset, the region investigated, and the visual contents used

The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings

Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both. However, other work has proposed that representations of drawings and natural images become similar only after substantial processing has taken place, suggesting distinct mechanisms. To arbitrate between those alternatives, we measured brain responses resolved in space and time using fMRI and MEG, respectively, while human participants (female and male) viewed images of objects depicted as photographs, line drawings, or sketch-like drawings. Using multivariate decoding, we demonstrate that object category information emerged similarly fast and across overlapping regions in occipital, ventral-temporal, and posterior parietal cortex for all types of depiction, yet with smaller effects at higher levels of visual abstraction. In addition, cross-decoding between depiction types revealed strong generalization of object category information from early processing stages on. Finally, by combining fMRI and MEG data using representational similarity analysis, we found that visual information traversed similar processing stages for all types of depiction, yet with an overall stronger representation for photographs. Together, our results demonstrate broad commonalities in the neural dynamics of object recognition across types of depiction, thus providing clear evidence for shared neural mechanisms underlying recognition of natural object images and abstract drawings

Reliability and generalizability of similarity-based fusion of meg and fmri data in human ventral and dorsal visual streams

To build a representation of what we see, the human brain recruits regions throughout the visual cortex in cascading sequence. Recently, an approach was proposed to evaluate the dynamics of visual perception in high spatiotemporal resolution at the scale of the whole brain. This method combined functional magnetic resonance imaging (fMRI) data with magnetoencephalography (MEG) data using representational similarity analysis and revealed a hierarchical progression from primary visual cortex through the dorsal and ventral streams. To assess the replicability of this method, we here present the results of a visual recognition neuro-imaging fusion experiment and compare them within and across experimental settings. We evaluated the reliability of this method by assessing the consistency of the results under similar test conditions, showing high agreement within participants. We then generalized these results to a separate group of individuals and visual input by comparing them to the fMRI-MEG fusion data of Cichy et al (2016), revealing a highly similar temporal progression recruiting both the dorsal and ventral streams. Together these results are a testament to the reproducibility of the fMRI-MEG fusion approach and allows for the interpretation of these spatiotemporal dynamic in a broader context

Similarity-based fusion of MEG and fMRI discerns early feedforward and feedback processing in the ventral stream

Successful models of vision, such as DNNs and HMAX, are inspired by the human visual system, relying on a hierarchical cascade of feedforward transformations akin to the ventral stream. Despite these advances, the human visual cortex remains unique in complexity, with feedforward and feedback pathways characterized by rapid spatiotemporal dynamics as visual information is transformed into semantic content. Thus, a systematic characterization of the spatiotemporal and representational space of the ventral visual pathway can offer novel insights in the duration and sequencing of cognitive processes, suggesting computational constraints and new architectures for computer vision models. To discern the feedforward and feedback neural processes underlying human vision, we used MEG/fMRI fusion. We collected MEG data while observers viewed a rapid-serial-visual-presentation of 11 images with an extremely fast speed (17ms/picture or 34ms/picture). Participants performed a two-alternative forced choice task reporting whether the middle image is a face or non-face. fMRI data while observers viewed the same stimuli were also collected. We used MVPA to pairwise compare all stimuli, creating RDMs separately for MEG and fMRI data. Comparison of time-resolved MEG-RDMs with space-resolved fMRI-RDMs yielded a spatiotemporal description of the ventral stream dynamics. Starting from EVC, brain activation progressed rapidly to IT within approximately 110ms from stimulus onset. The activation cascade reversed back to EVC at around 170ms. This was accompanied by a strengthening of IT activation, leading to categorical representations enhancement. The presented well-defined spatiotemporal dynamics can be used as constraints for developing new computational neuroscience models with recursive processes, to increase performances in challenging visual conditions

Deep convolutional neural networks, features, and categories perform similarly at explaining primate high-level visual representations

Deep convolutional neural networks (DNNs) are currently the best computational model for explaining image representations across the visual cortical hierarchy. However, it is unclear how the representations in DNNs relate to those of simpler “oracle” models of features and categories. We obtained DNN (AlexNet) representations for a set of 92 real-world object images. Human observers generated category and feature labels for the images. Category labels included subordinate, basic and superordinate categories; feature labels included object parts, colors, textures, and contours. We used the AlexNet representations and labels to explain brain representations of the images, measured with fMRI in humans and cell recordings in monkeys. For both human and monkey inferior temporal (IT) cortex, late AlexNet layers perform similarly to basic categories and object parts. Furthermore, late AlexNet layers can account for more than half of the variance that these labels explain in IT. Finally, while feature and category models predominantly explain image representations in high-level visual cortex, AlexNet layers explain representations across the entire visual cortical hierarchy. DNNs may provide a computationally explicit model of how features and categories are computed by the brain.This work was funded by a Sir Henry Wellcome Postdoctoral Fellowship (206521/Z/17/Z) to KMJ, a DFG grant CI 241-1/1 (Emmy Noether Programme) to RMC and a British Academy Postdoctoral Fellowship (PS 140117) to MM