A population receptive field model of the magnetoencephalography response.

Computational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). However, pRF models are not widely used for non-invasive electromagnetic field measurements (EEG/MEG), because individual sensors pool responses originating from several centimeter of cortex, containing neural populations with widely varying spatial tuning. Here, we introduce a forward-modeling approach in which pRFs estimated from fMRI data are used to predict MEG sensor responses. Subjects viewed contrast-reversing bar stimuli sweeping across the visual field in separate fMRI and MEG sessions. Individual subject's pRFs were modeled on the cortical surface at the millimeter scale using the fMRI data. We then predicted cortical time series and projected these predictions to MEG sensors using a biophysical MEG forward model, accounting for the pooling across cortex. We compared the predicted MEG responses to observed visually evoked steady-state responses measured in the MEG session. We found that pRF parameters estimated by fMRI could explain a substantial fraction of the variance in steady-state MEG sensor responses (up to 60% in individual sensors). Control analyses in which we artificially perturbed either pRF size or pRF position reduced MEG prediction accuracy, indicating that MEG data are sensitive to pRF properties derived from fMRI. Our model provides a quantitative approach to link fMRI and MEG measurements, thereby enabling advances in our understanding of spatiotemporal dynamics in human visual field maps.

Topographic numerosity maps cover subitizing and estimation ranges.

Numerosity, the set size of a group of items, helps guide behaviour and decisions. Non-symbolic numerosities are represented by the approximate number system. However, distinct behavioural performance suggests that small numerosities, i.e. subitizing range, are implemented differently in the brain than larger numerosities. Prior work has shown that neural populations selectively responding (i.e. hemodynamic responses) to small numerosities are organized into a network of topographical maps. Here, we investigate how neural populations respond to large numerosities, well into the ANS. Using 7 T fMRI and biologically-inspired analyses, we found a network of neural populations tuned to both small and large numerosities organized within the same topographic maps. These results demonstrate a continuum of numerosity preferences that progressively cover both the subitizing range and beyond within the same numerosity map, suggesting a single neural mechanism. We hypothesize that differences in map properties, such as cortical magnification and tuning width, underlie known differences in behaviour.

Individualized cognitive neuroscience needs 7T: Comparing numerosity maps at 3T and 7T MRI.

The field of cognitive neuroscience is weighing evidence about whether to move from the current standard field strength of 3 Tesla (3T) to ultra-high field (UHF) of 7T and above. The present study contributes to the evidence by comparing a computational cognitive neuroscience paradigm at 3T and 7T. The goal was to evaluate the practical effects, i.e. model predictive power, of field strength on a numerosity task using accessible pre-processing and analysis tools. Previously, using 7T functional magnetic resonance imaging and biologically-inspired analyses, i.e. population receptive field modelling, we discovered topographical organization of numerosity-selective neural populations in human parietal cortex. Here we show that these topographic maps are also detectable at 3T. However, averaging of many more functional runs was required at 3T to reliably reconstruct numerosity maps. On average, one 7T run had about four times the model predictive power of one 3T run. We believe that this amount of scanning would have made the initial discovery of the numerosity maps on 3T highly infeasible in practice. Therefore, we suggest that the higher signal-to-noise ratio and signal sensitivity of UHF MRI is necessary to build mechanistic models of the organization and function of our cognitive abilities in individual participants.

Topographic maps and neural tuning for sensory substitution dimensions learned in adulthood in a congenital blind subject.

Topographic maps, a key principle of brain organization, emerge during development. It remains unclear, however, whether topographic maps can represent a new sensory experience learned in adulthood. MaMe, a congenitally blind individual, has been extensively trained in adulthood for perception of a 2D auditory-space (soundscape) where the y- and x-axes are represented by pitch and time, respectively. Using population receptive field mapping we found neural populations tuned topographically to pitch, not only in the auditory cortices but also in the parietal and occipito-temporal cortices. Topographic neural tuning to time was revealed in the parietal and occipito-temporal cortices. Some of these maps were found to represent both axes concurrently, enabling MaMe to represent unique locations in the soundscape space. This case study provides proof of concept for the existence of topographic maps tuned to the newly learned soundscape dimensions. These results suggest that topographic maps can be adapted or recycled in adulthood to represent novel sensory experiences.

Change Blindness Is Influenced by Both Contrast Energy and Subjective Importance within Local Regions of the Image.

Our visual system receives an enormous amount of information, but not all information is retained. This is exemplified by the fact that subjects fail to detect large changes in a visual scene, i.e., change-blindness. Current theories propose that our ability to detect these changes is influenced by the gist or interpretation of an image. On the other hand, stimulus-driven image features such as contrast energy dominate the representation in early visual cortex (De Valois and De Valois, 1988; Boynton et al., 1999; Olman et al., 2004; Mante and Carandini, 2005; Dumoulin et al., 2008). Here we investigated whether contrast energy contributes to our ability to detect changes within a visual scene. We compared the ability to detect changes in contrast energy together with changes to a measure of the interpretation of an image. We used subjective important aspects of the image as a measure of the interpretation of an image. We measured reaction times while manipulating the contrast energy and subjective important properties using the change blindness paradigm. Our results suggest that our ability to detect changes in a visual scene is not only influenced by the subjective importance, but also by contrast energy. Also, we find that contrast energy and subjective importance interact. We speculate that contrast energy and subjective important properties are not independently represented in the visual system. Thus, our results suggest that the information that is retained of a visual scene is both influenced by stimulus-driven information as well as the interpretation of a scene.

Image identification from brain activity using the population receptive field model.

A goal of computational models is not only to explain experimental data but also to make new predictions. A current focus of computational neuroimaging is to predict features of the presented stimulus from measured brain signals. These computational neuroimaging approaches may be agnostic about the underlying neural processes or may be biologically inspired. Here, we use the biologically inspired population receptive field (pRF) approach to identify presented images from fMRI recordings of the visual cortex, using an explicit model of the underlying neural response selectivity. The advantage of the pRF-model is its simplicity: it is defined by a handful of parameters, which can be estimated from fMRI data that was collected within half an hour. Using 7T MRI, we measured responses elicited by different visual stimuli: (i) conventional pRF mapping stimuli, (ii) semi-random synthetic images and (iii) natural images. The pRF mapping stimuli were used to estimate the pRF-properties of each cortical location in early visual cortex. Next, we used these pRFs to identify which synthetic or natural images was presented to the subject from the fMRI responses. We show that image identification using V1 responses is far above chance, both for the synthetic and natural images. Thus, we can identify visual images, including natural images, using the most fundamental low-parameter pRF model estimated from conventional pRF mapping stimuli. This allows broader application of image identification.

In vivo evidence of functional and anatomical stripe-based subdivisions in human V2 and V3.

Visual cortex contains a hierarchy of visual areas. The earliest cortical area (V1) contains neurons responding to colour, form and motion. Later areas specialize on processing of specific features. The second visual area (V2) in non-human primates contains a stripe-based anatomical organization, initially defined using cytochrome-oxidase staining of post-mortem tissue. Neurons in these stripes have been proposed to serve distinct functional specializations, e.g. processing of color, form and motion. These stripes represent an intermediate stage in visual hierarchy and serve a key role in the increasing functional specialization of visual areas. Using sub-millimeter high-field functional and anatomical MRI (7T), we provide in vivo evidence for stripe-based subdivisions in humans. Using functional MRI, we contrasted responses elicited by stimuli alternating at slow and fast temporal frequencies. We revealed stripe-based subdivisions in V2 ending at the V1/V2 border. The human stripes reach into V3. Using anatomical MRI optimized for myelin contrast within gray matter, we also observe a stripe pattern. Stripe subdivisions preferentially responding to fast temporal frequencies are more myelinated. As such, functional and anatomical measures provide independent and converging evidence for functional organization into striped-based subdivisions in human V2 and V3.

Modeling center-surround configurations in population receptive fields using fMRI.

Antagonistic center-surround configurations are a central organizational principle of our visual system. In visual cortex, stimulation outside the classical receptive field can decrease neural activity and also decrease functional Magnetic Resonance Imaging (fMRI) signal amplitudes. Decreased fMRI amplitudes below baseline-0% contrast-are often referred to as "negative" responses. Using neural model-based fMRI data analyses, we can estimate the region of visual space to which each cortical location responds, i.e., the population receptive field (pRF). Current models of the pRF do not account for a center-surround organization or negative fMRI responses. Here, we extend the pRF model by adding surround suppression. Where the conventional model uses a circular symmetric Gaussian function to describe the pRF, the new model uses a circular symmetric difference-of-Gaussians (DoG) function. The DoG model allows the pRF analysis to capture fMRI signals below baseline and surround suppression. Comparing the fits of the models, an increased variance explained is found for the DoG model. This improvement was predominantly present in V1/2/3 and decreased in later visual areas. The improvement of the fits was particularly striking in the parts of the fMRI signal below baseline. Estimates for the surround size of the pRF show an increase with eccentricity and over visual areas V1/2/3. For the suppression index, which is based on the ratio between the volumes of both Gaussians, we show a decrease over visual areas V1 and V2. Using non-invasive fMRI techniques, this method gives the possibility to examine assumptions about center-surround receptive fields in human subjects.