Biology subjects

Cichy, R. M.

Publications and source records attributed to Cichy, R. M..

9 recordsLinked to original sources

bioRxiv2018

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, here we present 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.

neuroscience↗

bioRxiv2018

Typical visual-field locations facilitate access to awareness for everyday objects

In real-world vision, humans are constantly confronted with complex environments that contain a multitude of objects. These environments are spatially structured, so that objects have different likelihoods of appearing in specific parts of the visual space. Our massive experience with such positional regularities prompts the hypothesis that the processing of individual objects varies in efficiency across the visual field: when objects are encountered in their typical locations (e.g., we are used to seeing lamps in the upper visual field and carpets in the lower visual field), they should be more efficiently perceived than when they are encountered in atypical locations (e.g., a lamp in the lower visual field and a carpet in the upper visual field). Here, we provide evidence for this hypothesis by showing that typical positioning facilitates an objects access to awareness. In two continuous flash suppression experiments, objects more efficiently overcame inter-ocular suppression when they were presented in visual-field locations that matched their typical locations in the environment, as compared to non-typical locations. This finding suggests that through extensive experience the visual system has adapted to the statistics of the environment. This adaptation may be particularly useful for rapid object individuation in natural scenes.

neuroscience↗

bioRxiv2018

Finding decodable information that is read out in behaviour

Multivariate decoding methods applied to neuroimaging data have become the standard in cognitive neuroscience for unravelling statistical dependencies between brain activation patterns and experimental conditions. The current challenge is to demonstrate that information decoded as such by the experimenter is in fact used by the brain itself to guide behaviour. Here we demonstrate a promising approach to do so in the context of neural activation during object perception and categorisation behaviour. We first localised decodable information about visual objects in the human brain using a spatially-unbiased multivariate decoding analysis. We then related brain activation patterns to behaviour using a machine-learning based extension of signal detection theory. We show that while there is decodable information about visual category throughout the visual brain, only a subset of those representations predicted categorisation behaviour, located mainly in anterior ventral temporal cortex. Our results have important implications for the interpretation of neuroimaging studies, highlight the importance of relating decoding results to behaviour, and suggest a suitable methodology towards this aim.

neuroscience↗

bioRxiv2017

Scale-specific analysis of fMRI data on the irregular cortical surface

To fully characterize the activity patterns on the cerebral cortex as measured with fMRI, the spatial scale of the patterns must be ascertained. Here we address this problem by constructing steerable bandpass filters on the discrete, irregular cortical mesh, using an improved Gaussian smoothing in combination with differential operators of directional derivatives. We demonstrate the utility of the algorithm in two ways. First, using modelling we show that our algorithm yields superior results in numerical precision and spatial uniformity of filter kernels compared to the most widely adopted approach for cortical smoothing. An important interim insight hereby was that the effective scales of information differ from the nominal filter sizes applied to extract them, and thus need to be calculated separately to compare different algorithms on par. Second, we applied the algorithm to an fMRI dataset to assess the scale and pattern form of cortical encoding of information about visual objects in the ventral visual pathway. We found that filtering by our method improved the detection of discriminant information about experimental conditions over previous methods, that the level of categorization (subordinate versus superordinate) of objects was differentially related to the spatial scale of fMRI patterns, and that the spatial scale at which information was encoded increased along the ventral visual pathway. In sum, our results indicate that the proposed algorithm is particularly suited to assess and detect scale-specific information encoding in cortex, and promises further insight into the topography of cortical encoding in the human brain.

neuroscience↗

bioRxiv2017

Typical real-world locations impact the time course of object coding

In everyday visual environments, objects are non-uniformly distributed across visual space. Many objects preferentially occupy particular retinotopic locations: for example, lamps more often fall into the upper visual field, whereas carpets more often fall into the lower visual field. The long-term experience with natural environments prompts the hypothesis that the visual system is tuned to such retinotopic object locations. A key prediction is that typically positioned objects should be coded more efficiently. To test this prediction, we recorded electroencephalography (EEG) while participants viewed briefly presented objects appearing in their typical locations (e.g., an airplane in the upper visual field) or in atypical locations (e.g., an airplane in the lower visual field). Multivariate pattern analysis applied to the EEG data revealed that object classification depended on positional regularities: Objects were classified more accurately when positioned typically, rather than atypically, already at 140 ms, suggesting that relatively early stages of object processing are tuned to typical retinotopic locations. Our results confirm the prediction that long-term experience with objects occurring at specific locations leads to enhanced perceptual processing when these objects appear in their typical locations. This may indicate a neural mechanism for efficient natural scene processing, where a large number of typically positioned objects needs to be processed.

neuroscience↗

bioRxiv2017

Representational dynamics of task context and its influence on visual object processing

Despite the importance of an observers goals in determining how a visual object is categorized, surprisingly little is known about how humans process the task context in which objects occur and how it may interact with the processing of objects. Using magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) and multivariate techniques, we studied the spatial and temporal dynamics of task and object processing. Our results reveal a sequence of separate but overlapping task-related processes spread across frontoparietal and occipitotemporal cortex. Task exhibited late effects on object processing by selectively enhancing task-relevant object features, with limited impact on the overall pattern of object representations. Combining MEG and fMRI data, we reveal a parallel rise in task-related signals throughout the cerebral cortex, with an increasing dominance of task over object representations from early to higher visual areas. Collectively, our results reveal the complex dynamics underlying task and object representations throughout human cortex.

neuroscience↗

bioRxiv2017

Decoding the orientation of contrast edges from MEG evoked and induced responses

1Visual gamma oscillations have been proposed to subserve perceptual binding, but their strong modulation by diverse stimulus features confounds interpretations of their precise functional role. Overcoming this challenge necessitates a comprehensive account of the relationship between gamma responses and stimulus features. Here we used multivariate pattern analyses on human MEG data to characterize the relationships between gamma responses and one basic stimulus feature, the orientation of contrast edges. Our findings confirmed we could decode orientation information from induced responses in two dominant frequency bands at 24-32 Hz and 50-58 Hz. Decoding was higher for cardinal than oblique orientations, with similar results also obtained for evoked MEG responses. In contrast to multivariate analyses, orientation information was mostly absent in univariate signals: evoked and induced responses in early visual cortex were similar in all orientations, with only exception an inverse oblique effect observed in induced responses, such that cardinal orientations produced weaker oscillatory signals than oblique orientations. Taken together, our results showed multivariate methods are well suited for the analysis of gamma oscillations, with multivariate patterns robustly encoding orientation information and predominantly discriminating cardinal from oblique stimuli.

neuroscience↗

bioRxiv2017

Neural dynamics of real-world object vision that guide behaviour

1Vision involves complex neuronal dynamics that link the sensory stream to behaviour. To capture the richness and complexity of the visual world and the behaviour it entails, we used an ecologically valid task with a rich set of real-world object images. We investigated how human brain activity, resolved in space with functional MRI and in time with magnetoencephalography, links the sensory stream to behavioural responses. We found that behaviour-related brain activity emerged rapidly in the ventral visual pathway within 200ms of stimulus onset. The link between stimuli, brain activity, and behaviour could not be accounted for by either category membership or visual features (as provided by an artificial deep neural network model). Our results identify behaviourally-relevant brain activity during object vision, and suggest that object representations guiding behaviour are complex and can neither be explained by visual features or semantic categories alone. Our findings support the view that visual representations in the ventral visual stream need to be understood in terms of their relevance to behaviour, and highlight the importance of complex behavioural assessment for human brain mapping.

neuroscience↗

bioRxiv2016

Multivariate pattern analysis of MEG and EEG: a comparison of representational structure in time and space

1Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in early visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research.

neuroscience↗