How to use fMRI functional localizers to improve EEG/MEG source estimation

A phase-shifting anterior-posterior network organizes global phase relations

Prior research has identified a variety of task-dependent networks that form through inter-regional phase-locking of oscillatory activity that are neural correlates of specific behaviors. Despite ample knowledge of task-specific functional networks, general rules governing global phase relations have not been investigated. To discover such general rules, we focused on phase modularity, measured as the degree to which global phase relations in EEG comprised distinct synchronized clusters interacting with one another at large phase lags. Synchronized clusters were detected with a standard community-detection algorithm, and the degree of phase modularity was quantified by the index q. Notably, we found that the mechanism controlling phase modularity is remarkably simple. A network comprising anterior-posterior long-distance connectivity coherently shifted phase relations from low-angles (|Δθ| < π/4) in low-modularity states (bottom 5% in q) to high-angles (|Δθ| > 3π/4) in high-modularity states (top 5% in q), accounting for fluctuations in phase modularity. This anterior-posterior network may play a fundamental functional role as (1) it controls phase modularity across a broad range of frequencies (3-50 Hz examined) in different behavioral conditions (resting with the eyes closed or watching a silent nature video) and (2) neural interactions (measured as power correlations) in beta-to-gamma bands were consistently elevated in high-modularity states. These results may motivate future investigations into the functional roles of phase modularity as well as the anterior-posterior network that controls it.

Integration of Simultaneous fMRI and EEG source localization in emotional decision problems

Simultaneous EEG-fMRI has been a powerful technique to understand the mechanism of the brain in recent years. In this paper, we develop an integrating method by integrating the EEG data into the fMRI data based on the parametric empirical Bayesian (PEB) model to improve the accuracy of the brain source location. The gambling task, a classic paradigm, is used for the emotional decision-making study in this paper. The proposed method was conducted on 21 participants, including 16 men and 5 women. Contrary to the previous method that only localizes the area widely distributed across the ventral striatum and orbitofrontal cortex, the proposed method localizes accurately at the orbital frontal cortex during the process of the brain's emotional decision-making. The activated brain regions extracted by source localization were mainly located in the prefrontal and orbitofrontal lobes; the activation of the temporal pole regions unrelated to reward processing disappeared, and the activation of the somatosensory cortex and motor cortex was significantly reduced. The log evidence shows that the integration of simultaneous fMRI and EEG method based on synchronized data evidence is 22420, the largest value among the three methods. The integration method always takes on a larger value of log evidence and describes a better performance in analysis associated with source localization. DATA AVAILABILITY: The data used in the current study are available from the corresponding authouponon reasonable request.

Estimating neural activity from visual areas using functionally defined EEG templates

Electroencephalography (EEG) is a common and inexpensive method to record neural activity in humans. However, it lacks spatial resolution making it difficult to determine which areas of the brain are responsible for the observed EEG response. Here we present a new easy-to-use method that relies on EEG topographical templates. Using MRI and fMRI scans of 50 participants, we simulated how the activity in each visual area appears on the scalp and averaged this signal to produce functionally defined EEG templates. Once created, these templates can be used to estimate how much each visual area contributes to the observed EEG activity. We tested this method on extensive simulations and on real data. The proposed procedure is as good as bespoke individual source localization methods, robust to a wide range of factors, and has several strengths. First, because it does not rely on individual brain scans, it is inexpensive and can be used on any EEG data set, past or present. Second, the results are readily interpretable in terms of functional brain regions and can be compared across neuroimaging techniques. Finally, this method is easy to understand, simple to use and expandable to other brain sources.

The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoencephalography-functional magnetic resonance imaging study

Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the time-frequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.

Unveiling the phantom: What neuroimaging has taught us about phantom limb pain

Phantom limb pain (PLP) is a complicated condition with diverse clinical challenges. It consists of pain perception of a previously amputated limb. The exact pain mechanism is disputed and includes mechanisms involving cerebral, peripheral, and spinal origins. Such controversy limits researchers' and clinicians' ability to develop consistent therapeutics or management. Neuroimaging is an essential tool that can address this problem. This review explores diffusion tensor imaging, functional magnetic resonance imaging, electroencephalography, and magnetoencephalography in the context of PLP. These imaging modalities have distinct mechanisms, implications, applications, and limitations. Diffusion tensor imaging can outline structural changes and has surgical applications. Functional magnetic resonance imaging captures functional changes with spatial resolution and has therapeutic applications. Electroencephalography and magnetoencephalography can identify functional changes with a strong temporal resolution. Each imaging technique provides a unique perspective and they can be used in concert to reveal the true nature of PLP. Furthermore, researchers can utilize the respective strengths of each neuroimaging technique to support the development of innovative therapies. PLP exemplifies how neuroimaging and clinical management are intricately connected. This review can assist clinicians and researchers seeking a foundation for applications and understanding the limitations of neuroimaging techniques in the context of PLP.

A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex

Synchronization of neuronal responses over large distances is hypothesized to be important for many cortical functions. However, no straightforward methods exist to estimate synchrony non-invasively in the living human brain. MEG and EEG measure the whole brain, but the sensors pool over large, overlapping cortical regions, obscuring the underlying neural synchrony. Here, we developed a model from stimulus to cortex to MEG sensors to disentangle neural synchrony from spatial pooling of the instrument. We find that synchrony across cortex has a surprisingly large and systematic effect on predicted MEG spatial topography. We then conducted visual MEG experiments and separated responses into stimulus-locked and broadband components. The stimulus-locked topography was similar to model predictions assuming synchronous neural sources, whereas the broadband topography was similar to model predictions assuming asynchronous sources. We infer that visual stimulation elicits two distinct types of neural responses, one highly synchronous and one largely asynchronous across cortex.

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.

Principles and open questions in functional brain network reconstruction

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.

Non-invasive inference of information flow using diffusion MRI, functional MRI, and MEG

OBJECTIVE

To infer information flow in the white matter of the brain and recover cortical activity using functional MRI, diffusion MRI, and MEG without a manual selection of the white matter connections of interest.

APPROACH

A Bayesian network which encodes the priors knowledge of possible brain states is built from imaging data. Diffusion MRI is used to enumerate all possible connections between cortical regions. Functional MRI is used to prune connections without manual intervention and increase the likelihood of specific regions being active. MEG data is used as evidence into this network to obtain a posterior distribution on cortical regions and connections.

MAIN RESULTS

We show that our proposed method is able to identify connections associated with the a sensory-motor task. This allows us to build the Bayesian network with no manual selection of connections of interest. Using sensory-motor MEG evoked response as evidence into this network, our method identified areas known to be involved in a visuomotor task. In addition, information flow along white matter fiber bundles connecting those regions was also recovered.

SIGNIFICANCE

Current methods to estimate white matter information flow are extremely invasive, therefore limiting our understanding of the interaction between cortical regions. The proposed method makes use of functional MRI, diffusion MRI, and M/EEG to infer communication between cortical regions, therefore opening the door to the non-invasive exploration of information flow in the white matter.

Dietary Melatonin Therapy Alleviates the Lamina Cribrosa Damages in Patients with Mild Cognitive Impairments: A Double-Blinded, Randomized Controlled Study

BACKGROUND Alzheimer's disease (AD) is a degenerative disease that is characterized by massive neuron devastations in the hippocampus and cortex. Mild cognitive impairment (MCI) is the transitory stage between normality and AD dementia. This study aimed to investigate the melatonin induced effects on the lamina cribrosa thickness (LCT) of patients with MCI. MATERIAL AND METHODS The LCT data of patients with MCI were compared to LCT data of healthy controls. Subsequently, all MCI patients were randomly assigned into an experimental group (with melatonin treatment) or a placebo group (without any melatonin treatment). RESULTS The LCT of MCI patients decreased significantly compared with healthy controls. The univariate analysis showed that the lower the Mini Mental State Examination (MMSE) score (P=0.038; 95% CI: 0.876, -0.209), the smaller hippocampus volume (P=0.001; 95% CI: -1.594, -2.911), and the upregulated level of cerebrospinal fluid (CSF) T-tau (P=0.036; 95% CI: 2.546, -0.271) were associated significantly with the thinner LCT in MCI patients. There were 40 patients in the experimental group and 39 patients in the placebo group. The mean age of the experimental group was not significantly different from the placebo group (66.3±8.8 versus 66.5±8.3; P>0.05). The LCT and hippocampus volume of the melatonin treated group were significantly larger compared with the placebo group (P<0.001). On the other hand, the CSF T-tau level of the melatonin treated group was significantly lower compared with the untreated group (P<0.001). CONCLUSIONS LCT assessment might allow early diagnosis of MCI. Dietary melatonin therapy could provide an effective medication for MCI patients with LCT alterations.

Contrast Normalization Accounts for Binocular Interactions in Human Striate and Extra-striate Visual Cortex

During binocular viewing, visual inputs from the two eyes interact at the level of visual cortex. Here we studied binocular interactions in human visual cortex, including both sexes, using source-imaged steady-state visual evoked potentials over a wide range of relative contrast between two eyes. The ROIs included areas V1, V3a, hV4, hMT⁺, and lateral occipital cortex. Dichoptic parallel grating stimuli in each eye modulated at distinct temporal frequencies allowed us to quantify spectral components associated with the individual stimuli from monocular inputs (self-terms) and responses due to interaction between the inputs from the two eyes (intermodulation [IM] terms). Data with self-terms revealed an interocular suppression effect, in which the responses to the stimulus in one eye were reduced when a stimulus was presented simultaneously to the other eye. The suppression magnitude varied depending on visual area, and the relative contrast between the two eyes. Suppression was strongest in V1 and V3a (50% reduction) and was least in lateral occipital cortex (20% reduction). Data with IM terms revealed another form of binocular interaction, compared with self-terms. IM response was strongest at V1 and was least in hV4. Fits of a family of divisive gain control models to both self- and IM-term responses within each cortical area indicated that both forms of binocular interaction shared a common gain control nonlinearity. However, our model fits revealed different patterns of binocular interaction along the cortical hierarchy, particularly in terms of excitatory and suppressive contributions.SIGNIFICANCE STATEMENT Using source-imaged steady-state visual evoked potentials and frequency-domain analysis of dichoptic stimuli, we measured two forms of binocular interactions: one is associated with the individual stimuli that represent interocular suppression from each eye, and the other is a direct measure of interocular interaction between inputs from the two eyes. We demonstrated that both forms of binocular interactions share a common gain control mechanism in striate and extra-striate cortex. Furthermore, our model fits revealed different patterns of binocular interaction along the visual cortical hierarchy, particularly in terms of excitatory and suppressive contributions.

EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise

BACKGROUND

Electroencephalography (EEG) is widely used to investigate human brain function. Simulation studies are essential for assessing the validity of EEG analysis methods and the interpretability of results.

NEW METHOD

Here we present a simulation environment for generating EEG data by embedding biologically plausible signal and noise into MRI-based forward models that incorporate individual-subject variability in structure and function.

RESULTS

The package includes pipelines for the evaluation and validation of EEG analysis tools for source estimation, functional connectivity, and spatial filtering. EEG dynamics can be simulated using realistic noise and signal models with user specifiable signal-to-noise ratio (SNR). We also provide a set of quantitative metrics tailored to source estimation, connectivity and spatial filtering applications.

COMPARISON WITH EXISTING METHOD(S)

We provide a larger set of forward solutions for individual MRI-based head models than has been available previously. These head models are surface-based and include two sets of regions-of-interest (ROIs) that have been brought into registration with the brain of each individual using surface-based alignment - one from a whole brain and the other from a visual cortex atlas. We derive a realistic model of noise by fitting different model components to measured resting state EEG. We also provide a set of quantitative metrics for evaluating source-localization, functional connectivity and spatial filtering methods.

CONCLUSIONS

The inclusion of a larger number of individual head-models, combined with surface-atlas based labeling of ROIs and plausible models of signal and noise, allows for simulation of EEG data with greater realism than previous packages.

Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI

Joint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of experimental design choices on jICA performance has not been systematically studied. Here, the sensitivity of jICA for recovering neural sources in individual data was evaluated as a function of imaging SNR, number of independent representations of the ERP/fMRI data, relationship between instantiations of the joint ERP/fMRI activity (linear, non-linear, uncoupled), and type of sources (varying parametrically and non-parametrically across representations of the data), using computer simulations. Neural sources were simulated with spatiotemporal and noise attributes derived from experimental data. The best performance, maximizing both cross-modal data fusion and the separation of brain sources, occurred with a moderate number of representations of the ERP/fMRI data (10-30), as in a mixed block/event related experimental design. Importantly, the type of relationship between instantiations of the ERP/fMRI activity, whether linear, non-linear or uncoupled, did not in itself impact jICA performance, and was accurately recovered in the common profiles (i.e., mixing coefficients). Thus, jICA provides an unbiased way to characterize the relationship between ERP and fMRI activity across brain regions, in individual data, rendering it potentially useful for characterizing pathological conditions in which neurovascular coupling is adversely affected.

EEG/MEG source imaging using fMRI informed time-variant constraints

Multimodal functional neuroimaging by combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) or magnetoencephalography (MEG) is able to provide high spatiotemporal resolution mapping of brain activity. However, the accuracy of fMRI-constrained EEG/MEG source imaging may be degraded by potential spatial mismatches between the locations of fMRI activation and electrical source activities. To address this problem, we propose a novel fMRI informed time-variant constraint (FITC) method. The weights in FITC are determined by combining the fMRI activities and electrical source activities in a time-variant manner to reduce the impact of the fMRI extra sources. The fMRI weights are modified using cross-talk matrix and normalized partial area under the curve to reduce the impact of fMRI missing sources. Monte Carlo simulations were performed to compare the source estimates produced by L2-minimum norm estimation (MNE), fMRI-weighted minimum norm estimation (fMNE), FITC, and depth-weighted FITC (wFITC) algorithms with various spatial mismatch conditions. Localization error and temporal correlation were calculated to compare the four algorithms under different conditions. The simulation results indicated that the FITC and wFITC methods were more robust than the MNE and fMNE algorithms. Moreover, FITC and wFITC were significantly better than fMNE under the fMRI missing sources condition. A human visual-stimulus EEG, MEG, and fMRI test was performed, and the experimental data revealed that FITC and wFITC displayed more focal areas than fMNE and MNE. In conclusion, the proposed FITC method is able to better resolve the spatial mismatch problems encountered in fMRI-constrained EEG/MEG source imaging.

Dynamics of perceptual decisions about symmetry in visual cortex

Neuroimaging studies have identified multiple extra-striate visual areas that are sensitive to symmetry in planar images (Kohler et al., 2016; Sasaki et al., 2005). Here, we investigated which of these areas are directly involved in perceptual decisions about symmetry, by recording high-density EEG in participants (n = 25) who made rapid judgments about whether an exemplar image contained rotation symmetry or not. Stimulus-locked sensor-level analysis revealed symmetry-specific activity that increased with increasing order of rotation symmetry. Response-locked analysis identified activity occurring between 600 and 200 ms before the button-press, that was directly related to perceptual decision making. We then used fMRI-informed EEG source imaging to characterize the dynamics of symmetry-specific activity within an extended network of areas in visual cortex. The most consistent cortical source of the stimulus-locked activity was VO1, a topographically organized area in ventral visual cortex, that was highly sensitive to symmetry in a previous study (Kohler et al., 2016). Importantly, VO1 activity also contained a strong decision-related component, suggesting that this area plays a crucial role in perceptual decisions about symmetry. Other candidate areas, such as lateral occipital cortex, had weak stimulus-locked symmetry responses and no evidence of correlation with response timing.

Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential

The most widespread measures of human brain activity are the blood-oxygen-level dependent (BOLD) signal and surface field potential. Prior studies report a variety of relationships between these signals. To develop an understanding of how to interpret these signals and the relationship between them, we developed a model of (a) neuronal population responses and (b) transformations from neuronal responses into the functional magnetic resonance imaging (fMRI) BOLD signal and electrocorticographic (ECoG) field potential. Rather than seeking a transformation between the two measures directly, this approach interprets each measure with respect to the underlying neuronal population responses. This model accounts for the relationship between BOLD and ECoG data from human visual cortex in V1, V2, and V3, with the model predictions and data matching in three ways: across stimuli, the BOLD amplitude and ECoG broadband power were positively correlated, the BOLD amplitude and alpha power (8-13 Hz) were negatively correlated, and the BOLD amplitude and narrowband gamma power (30-80 Hz) were uncorrelated. The two measures provide complementary information about human brain activity, and we infer that features of the field potential that are uncorrelated with BOLD arise largely from changes in synchrony, rather than level, of neuronal activity.

The effects of changes in object location on object identity detection: A simultaneous EEG-fMRI study

Object identity and location are bound together to form a unique integration that is maintained and processed in visual working memory (VWM). Changes in task-irrelevant object location have been shown to impair the retrieval of memorial representations and the detection of object identity changes. However, the neural correlates of this cognitive process remain largely unknown. In the present study, we aim to investigate the underlying brain activation during object color change detection and the modulatory effects of changes in object location and VWM load. To this end we used simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings, which can reveal the neural activity with both high temporal and high spatial resolution. Subjects responded faster and with greater accuracy in the repeated compared to the changed object location condition, when a higher VWM load was utilized. These results support the spatial congruency advantage theory and suggest that it is more pronounced with higher VWM load. Furthermore, the spatial congruency effect was associated with larger posterior N1 activity, greater activation of the right inferior frontal gyrus (IFG) and less suppression of the right supramarginal gyrus (SMG), when object location was repeated compared to when it was changed. The ERP-fMRI integrative analysis demonstrated that the object location discrimination-related N1 component is generated in the right SMG.

Sparse EEG/MEG source estimation via a group lasso

Non-invasive recordings of human brain activity through electroencephalography (EEG) or magnetoencelphalography (MEG) are of value for both basic science and clinical applications in sensory, cognitive, and affective neuroscience. Here we introduce a new approach to estimating the intra-cranial sources of EEG/MEG activity measured from extra-cranial sensors. The approach is based on the group lasso, a sparse-prior inverse that has been adapted to take advantage of functionally-defined regions of interest for the definition of physiologically meaningful groups within a functionally-based common space. Detailed simulations using realistic source-geometries and data from a human Visual Evoked Potential experiment demonstrate that the group-lasso method has improved performance over traditional ℓ2 minimum-norm methods. In addition, we show that pooling source estimates across subjects over functionally defined regions of interest results in improvements in the accuracy of source estimates for both the group-lasso and minimum-norm approaches.

Cortical sources of Vernier acuity in the human visual system: An EEG-source imaging study

Vernier acuity determines the relative position of visual features with a precision better than the sampling resolution of cone receptors in the retina. Because Vernier displacement is thought to be mediated by orientation-tuned mechanisms, Vernier acuity is presumed to be processed in striate visual cortex (V1). However, there is considerable evidence suggesting that Vernier acuity is dependent not only on structures in V1 but also on processing in extrastriate cortical regions. Here we used functional magnetic resonance imaging–informed electroencephalogram source imaging to localize the cortical sources of Vernier acuity in observers with normal vision. We measured suprathreshold and near-threshold responses to Vernier onset/offset stimuli at different stages of the visual cortical hierarchy, including V1, hV4, lateral occipital cortex (LOC), and middle temporal cortex (hMT+). These responses were compared with responses to grating on/off stimuli, as well as to stimuli that control for lateral motion in the Vernier task. Our results show that all visual cortical regions of interest (ROIs) responded to both suprathreshold Vernier and grating stimuli. However, thresholds for Vernier displacement (Vernier acuity) were lowest in V1 and LOC compared with hV4 and hMT+, whereas all visual ROIs had identical thresholds for spatial frequency (grating acuity) and for relative motion. The cortical selectivity of sensitivity to Vernier displacement provides strong evidence that LOC, in addition to V1, is involved in Vernier acuity processing. The robust activation of LOC might be related to the sensitivity to the relative position of features, which is common to Vernier displacement and to some kinds of texture segmentation.

Degraded attentional modulation of cortical neural populations in strabismic amblyopia

Behavioral studies have reported reduced spatial attention in amblyopia, a developmental disorder of spatial vision. However, the neural populations in the visual cortex linked with these behavioral spatial attention deficits have not been identified. Here, we use functional MRI–informed electroencephalography source imaging to measure the effect of attention on neural population activity in the visual cortex of human adult strabismic amblyopes who were stereoblind. We show that compared with controls, the modulatory effects of selective visual attention on the input from the amblyopic eye are substantially reduced in the primary visual cortex (V1) as well as in extrastriate visual areas hV4 and hMT+. Degraded attentional modulation is also found in the normal-acuity fellow eye in areas hV4 and hMT+ but not in V1. These results provide electrophysiological evidence that abnormal binocular input during a developmental critical period may impact cortical connections between the visual cortex and higher level cortices beyond the known amblyopic losses in V1 and V2, suggesting that a deficit of attentional modulation in the visual cortex is an important component of the functional impairment in amblyopia. Furthermore, we find that degraded attentional modulation in V1 is correlated with the magnitude of interocular suppression and the depth of amblyopia. These results support the view that the visual suppression often seen in strabismic amblyopia might be a form of attentional neglect of the visual input to the amblyopic eye.

Representation of Maximally Regular Textures in Human Visual Cortex

Naturalistic textures with an intermediate degree of statistical regularity can capture key structural features of natural images (Freeman and Simoncelli, 2011). V2 and later visual areas are sensitive to these features, while primary visual cortex is not (Freeman et al., 2013). Here we expand on this work by investigating a class of textures that have maximal formal regularity, the 17 crystallographic wallpaper groups (Fedorov, 1891). We used texture stimuli from four of the groups that differ in the maximum order of rotation symmetry they contain, and measured neural responses in human participants using functional MRI and high-density EEG. We found that cortical area V3 has a parametric representation of the rotation symmetries in the textures that is not present in either V1 or V2, the first discovery of a stimulus property that differentiates processing in V3 from that of lower-level areas. Parametric responses were also seen in higher-order ventral stream areas V4, VO1, and lateral occipital complex (LOC), but not in dorsal stream areas. The parametric response pattern was replicated in the EEG data, and source localization indicated that responses in V3 and V4 lead responses in LOC, which is consistent with a feedforward mechanism. Finally, we presented our stimuli to four well developed feedforward models and found that none of them were able to account for our results. Our results highlight structural regularity as an important stimulus dimension for distinguishing the early stages of visual processing, and suggest a previously unrecognized role for V3 in the visual form-processing hierarchy. Significance statement: Hierarchical processing is a fundamental organizing principle in visual neuroscience, with each successive processing stage being sensitive to increasingly complex stimulus properties. Here, we probe the encoding hierarchy in human visual cortex using a class of visual textures--wallpaper patterns--that are maximally regular. Through a combination of fMRI and EEG source imaging, we find specific responses to texture regularity that depend parametrically on the maximum order of rotation symmetry in the textures. These parametric responses are seen in several areas of the ventral visual processing stream, as well as in area V3, but not in V1 or V2. This is the first demonstration of a stimulus property that differentiates processing in V3 from that of lower-level visual areas.

Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data

The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. In this manuscript, we show how a method for integrating three data modalities within a single framework provides (1) more detailed descriptions of cognitive processes and (2) more accurate predictions of unobserved data than less integrative methods. Specifically, we show how combining either EEG and fMRI with a behavioral model can perform substantially better than a behavioral-data-only model in both generative and predictive modeling analyses. We then show how a trivariate model - a model including EEG, fMRI, and behavioral data - outperforms bivariate models in both generative and predictive modeling analyses. Together, these results suggest that within an appropriate modeling framework, more data can be used to better constrain cognitive theory, and to generate more accurate predictions for behavioral and neural data.

Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data

A new method for the analysis and localization of brain activity has been developed, based on multichannel magnetic field recordings, over minutes, superimposed on the MRI of the individual. Here, a high resolution Fourier Transform is obtained over the entire recording period, leading to a detailed multi-frequency spectrum. Further analysis implements a total decomposition of the frequency components into functionally invariant entities, each having an invariant field pattern localizable in recording space. The method, addressed as functional tomography, makes it possible to find the distribution of magnetic field sources in space. Here, the method is applied to the analysis of simulated data, to oscillating signals activating a physical current dipoles phantom, and to recordings of spontaneous brain activity in 10 healthy adults. In the analysis of simulated data, 61 dipoles are localized with 0.7 mm precision. Concerning the physical phantom the method is able to localize three simultaneously activated current dipoles with 1 mm precision. Spatial resolution 3 mm was attained when localizing spontaneous alpha rhythm activity in 10 healthy adults, where the alpha peak was specified for each subject individually. Co-registration of the functional tomograms with each subject's head MRI localized alpha range activity to the occipital and/or posterior parietal brain region. This is the first application of this new functional tomography to human brain activity. The method successfully provides an overall view of brain electrical activity, a detailed spectral description and, combined with MRI, the localization of sources in anatomical brain space.

Intraparietal regions play a material general role in working memory: Evidence supporting an internal attentional role

Determining the role of intraparietal sulcus (IPS) regions in working memory (WM) remains a topic of considerable interest and lack of clarity. One group of hypotheses, the internal attention view, proposes that the IPS plays a material general role in maintaining information in WM. An alternative viewpoint, the pure storage account, proposes that the IPS in each hemisphere maintains material specific (e.g., left--phonological; right--visuospatial) information. Yet, adjudication between competing theoretical perspectives is complicated by divergent findings from different methodologies and their use of different paradigms, perhaps most notably between functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). For example, fMRI studies typically use full field stimulus presentations and report bilateral IPS activation, whereas EEG studies direct attention to a single hemifield and report a contralateral bias in both hemispheres. Here, we addressed this question by applying a regions-of-interest fMRI approach to elucidate IPS contributions to WM. Importantly, we manipulated stimulus type (verbal, visuospatial) and the cued hemifield to assess the degree to which IPS activations reflect stimulus specific or stimulus general processing consistent with the pure storage or internal attention hypotheses. These data revealed significant contralateral bias along regions IPS0-5 regardless of stimulus type. Also present was a weaker stimulus-based bias apparent in stronger left lateralized activations for verbal stimuli and stronger right lateralized activations for visuospatial stimuli. However, there was no consistent stimulus-based lateralization of activity. Thus, despite the observation of stimulus-based modulation of spatial lateralization this pattern was bilateral. As such, although it is quantitatively underspecified, our results are overall more consistent with an internal attention view that the IPS plays a material general role in refreshing the contents of WM.

The steady-state visual evoked potential in vision research: A review

Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.