Biology subjects

Linda Henriksson

Publications and source records attributed to Linda Henriksson.

4 recordsLinked to original sources

bioRxiv2016

Cortical dynamics of saccade-target selection during free-viewing of natural scenes

Natural visual behaviour entails explorative eye movements, saccades, that bring different parts of a visual scene into the central vision. The neural processes guiding the selection of saccade targets are still largely unknown. Therefore, in this study, we tracked with magnetoencephalography (MEG) cortical dynamics of viewers who were freely exploring novel natural scenes. Overall, the viewers were largely consistent in their gaze behaviour, especially if the scene contained any persons. We took a fresh approach to relate the eye-gaze data to the MEG signals by characterizing dynamic cortical representations by means of representational distance matrices. Specifically, we compared the representational distances between the stimuli in the evoked MEG responses with predictions based (1) on the low-level visual similarity of the stimuli (as visually more similar stimuli evoke more similar responses in early visual areas) and (2) on the eye-gaze data. At 50-75 ms after the scene onset, the similarity of the occipital MEG patterns correlated with the low-level visual similarity of the scenes, and already at 75-100 ms the visual features attracting the first saccades predicted the similarity of the right parieto-occipital MEG responses. Thereafter, at 100-125 ms, the landing positions of the upcoming saccades explained MEG responses. These results indicate that MEG signals contain signatures of the rapid processing of natural visual scenes as well as of the initiation of the first saccades, with the processing of the saccade target preceding the processing of the landing position of the upcoming saccade.\n\nSIGNIFICANCE STATEMENTHumans naturally make eye movements to bring different parts of a visual scene to the fovea where our visual acuity is the best. Tracking of eye gaze can reveal how we make inferences about the content of a scene by looking at different objects, or which visual cues automatically attract our attention and gaze. The brain dynamics governing natural gaze behaviour is still largely unknown. Here we suggest a novel approach to relate eye-tracking results with brain activity, as measured with magnetoencephalography (MEG), and demonstrate signatures of natural gaze behaviour in the MEG data already before the eye movements occur.

Neuroscience↗

bioRxiv2014

Faciotopy—a face-feature map with face-like topology in the human occipital face area

The occipital face area (OFA) and fusiform face area (FFA) are brain regions thought to be specialized for face perception. However, their intrinsic functional organization and status as cortical areas with well-defined boundaries remains unclear. Here we test these regions for \"faciotopy\", a particular hypothesis about their intrinsic functional organisation. A faciotopic area would contain a face-feature map on the cortical surface, where cortical patches represent face features and neighbouring patches represent features that are physically neighbouring in a face. The faciotopy hypothesis is motivated by the idea that face regions might develop from a retinotopic protomap and acquire their selectivity for face features through natural visual experience. Faces have a prototypical configuration of features, are usually perceived in a canonical upright orientation, and are frequently fixated in particular locations. To test the faciotopy hypothesis, we presented images of isolated face features at fixation to subjects during functional magnetic resonance imaging. The responses in V1 were best explained by low-level image properties of the stimuli. OFA, and to a lesser degree FFA, showed evidence for faciotopic organization. When a single patch of cortex was estimated for each face feature, the cortical distances between the feature patches reflected the physical distance between the features in a face. Faciotopy would be the first example, to our knowledge, of a cortical map reflecting the topology, not of a part of the organism itself (its retina in retinotopy, its body in somatotopy), but of an external object of particular perceptual significance.

Neuroscience↗

bioRxiv2014

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models

Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical areas representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network, and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies in the training set did not match their frequencies in natural experience or their behavioural importance. The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas. Our results demonstrate the benefits of testing both the specific representational hypothesis expressed by a models original feature space and the hypothesis space generated by linear transformations of that feature space.\n\nHighlightsO_LIWe tested computational models of representations in ventral-stream visual areas.\nC_LIO_LIWe compared representational dissimilarities with/without linear remixing of model features.\nC_LIO_LIEarly visual areas were best explained by shallow - and higher by deep - models.\nC_LIO_LIUnsupervised shallow models performed better without linear remixing of their features.\nC_LIO_LIA supervised deep convolutional net performed best with linear feature remixing.\nC_LI

Neuroscience↗

bioRxiv2014

Intrinsic cortical dynamics dominate population responses to natural images across human visual cortex

Intrinsic cortical dynamics are thought to underlie trial-to-trial variability of visually evoked responses in animal models. Understanding their function in the context of sensory processing and representation is a major current challenge. Here we report that intrinsic cortical dynamics strongly affect the representational geometry of a brain region, as reflected in response-pattern dissimilarities, and exaggerate the similarity of representations between brain regions. We characterized the representations in several human visual areas by representational dissimilarity matrices (RDMs) constructed from fMRI response-patterns for natural image stimuli. The RDMs of different visual areas were highly similar when the response-patterns were estimated on the basis of the same trials (sharing intrinsic cortical dynamics), and quite distinct when patterns were estimated on the basis of separate trials (sharing only the stimulus-driven component). We show that the greater similarity of the representational geometries can be explained by the coherent fluctuations of regional-mean activation within visual cortex, reflecting intrinsic dynamics. Using separate trials to study stimulus-driven representations revealed clearer distinctions between the representational geometries: a Gabor wavelet pyramid model explained representational geometry in visual areas V1-3 and a categorical animate- inanimate model in the object-responsive lateral occipital cortex.

Neuroscience↗