Four concurrent feedforward and feedback networks with different roles in the visual cortical hierarchy.

Visual stimuli evoke fast-evolving activity patterns that are distributed across multiple cortical areas. These areas are hierarchically structured, as indicated by their anatomical projections, but how large-scale feedforward and feedback streams are functionally organized in this system remains an important missing clue to understanding cortical processing. By analyzing visual evoked responses in laminar recordings from 6 cortical areas in awake mice, we uncovered a dominant feedforward network with scale-free interactions in the time domain. In addition, we established the simultaneous presence of a gamma band feedforward and 2 low frequency feedback networks, each with a distinct laminar functional connectivity profile, frequency spectrum, temporal dynamics, and functional hierarchy. We could identify distinct roles for each of these 4 processing streams, by leveraging stimulus contrast effects, analyzing receptive field (RF) convergency along functional interactions, and determining relationships to spiking activity. Our results support a dynamic dual counterstream view of hierarchical processing and provide new insight into how separate functional streams can simultaneously and dynamically support visual processes.

Neural sources of letter and Vernier acuity.

Visual acuity can be measured in many different ways, including with letters and Vernier offsets. Prior psychophysical work has suggested that the two acuities are strongly linked given that they both depend strongly on retinal eccentricity and both are similarly affected in amblyopia. Here we used high-density EEG recordings to ask whether the underlying neural sources are common as suggested by the psychophysics or distinct. To measure visual acuity for letters, we recorded evoked potentials to 3 Hz alternations between intact and scrambled text comprised of letters of varying size. To measure visual acuity for Vernier offsets, we recorded evoked potentials to 3 Hz alternations between bar gratings with and without a set of Vernier offsets. Both alternation types elicited robust activity at the 3 Hz stimulus frequency that scaled in amplitude with both letter and offset size, starting near threshold. Letter and Vernier offset responses differed in both their scalp topography and temporal dynamics. The earliest evoked responses to letters occurred on lateral occipital visual areas, predominantly over the left hemisphere. Later responses were measured at electrodes over early visual cortex, suggesting that letter structure is first extracted in second-tier extra-striate areas and that responses over early visual areas are due to feedback. Responses to Vernier offsets, by contrast, occurred first at medial occipital electrodes, with responses at later time-points being more broadly distributed-consistent with feedforward pathway mediation. The previously observed commonalities between letter and Vernier acuity may be due to common bottlenecks in early visual cortex but not because the two tasks are subserved by a common network of visual areas.

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.

Aging of human alpha rhythm.

Alpha rhythm (AR) changes are the most pronounced electroencephalogram phenomenon in the aging brain. We analyzed them based on the inherent AR structure obtained by parallel factor analysis decomposition in the cortical source space. AR showed a stable multicomponent structure in 78% of sixty 20- to 81-year-old healthy adults. Typically, it consists of 2 components. The distribution of the higher frequency occipito-parietal component widens with age, with its maximum moving from BA18/19 to BA37. The low-frequency component originating from the occipito-temporal regions in young adults also moves anteriorly with age, while maintaining its maximum within BA37. Both components slow down by 1 Hz over the adult lifespan. The multicomponent AR is more common in younger subjects, whereas a single-component AR in older subjects. This uneven occurrence as well as the increasing spatial and frequency overlaps between components suggest transformation of the multicomponent AR into the single-component AR with age. A detailed knowledge of AR component structure would be useful to monitor age-related neurodegenerative processes in humans.

Author Correction: Fine Structure of Posterior Alpha Rhythm in Human EEG: Frequency Components, Their Cortical Sources, and Temporal Behavior.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Fine Structure of Posterior Alpha Rhythm in Human EEG: Frequency Components, Their Cortical Sources, and Temporal Behavior.

Heterogeneity of the posterior alpha rhythm (AR) is a widely assumed but rarely tested phenomenon. We decomposed the posterior AR in the cortical source space with a 3-way PARAFAC technique, taking into account the spatial, frequency, and temporal aspects of mid-density EEG. We found a multicomponent AR structure in 90% of a group of 29 healthy adults. The typical resting-state structure consisted of a high-frequency occipito-parietal component of the AR (ARC1) and a low-frequency occipito-temporal component (ARC2), characterized by individual dynamics in time. In a few cases, we found a 3-component structure, with two ARC1s and one ARC2. The AR structures were stable in their frequency and spatial features over weeks to months, thus representing individual EEG alpha phenotypes. Cortical topography, individual stability, and similarity to the primate AR organization link ARC1 to the dorsal visual stream and ARC2 to the ventral one. Understanding how many and what kind of posterior AR components contribute to the EEG is essential for clinical neuroscience as an objective basis for AR segmentation and for interpreting AR dynamics under various conditions, both normal and pathological, which can selectively affect individual components.

Functional connectivity analysis in EEG source space: The choice of method.

Functional connectivity (FC) is among the most informative features derived from EEG. However, the most straightforward sensor-space analysis of FC is unreliable owing to volume conductance effects. An alternative-source-space analysis of FC-is optimal for high- and mid-density EEG (hdEEG, mdEEG); however, it is questionable for widely used low-density EEG (ldEEG) because of inadequate surface sampling. Here, using simulations, we investigate the performance of the two source FC methods, the inverse-based source FC (ISFC) and the cortical partial coherence (CPC). To examine the effects of localization errors of the inverse method on the FC estimation, we simulated an oscillatory source with varying locations and SNRs. To compare the FC estimations by the two methods, we simulated two synchronized sources with varying between-source distance and SNR. The simulations were implemented for hdEEG, mdEEG, and ldEEG. We showed that the performance of both methods deteriorates for deep sources owing to their inaccurate localization and smoothing. The accuracy of both methods improves with the increasing between-source distance. The best ISFC performance was achieved using hd/mdEEG, while the best CPC performance was observed with ldEEG. In conclusion, with hdEEG, ISFC outperforms CPC and therefore should be the preferred method. In the studies based on ldEEG, the CPC is a method of choice.

Perception-related EEG is more sensitive to Alzheimer's disease effects than resting EEG.

To characterize the effects of Alzheimer's disease (AD) on cortical functional connectivity in perception, we analyzed interhemispheric lagged synchronization (ILS) in the source space of high-density EEG recorded in aged controls and patients with amnestic mild cognitive impairment (aMCI) or AD while they viewed collinear and noncollinear bilateral moving gratings. Beta-band ILS was lower in aMCI and AD compared with controls in a large region centered on BA39. As previously reported, in young adults, collinear iso-oriented gratings versus noncollinear gratings synchronizes EEG reflecting perceptual grouping. Only aged controls showed the expected beta-band ILS increase originating in the dorsal visual stream (BA18). The aMCI group only showed a theta-band increase in an adjacent region (BA19). In AD patients, there was no ILS increase. Regression analysis revealed that the posterior callosal area and EEG slowing predict reduction of beta but not emergence of theta ILS response. Considering that we found no between-group differences in resting ILS, perception-related EEG appears to be more sensitive to AD effects, including ILS signs of neurodegeneration and compensation.

Weakened functional connectivity in patients with psychogenic non-epileptic seizures (PNES) converges on basal ganglia.

BACKGROUND: Psychogenic non-epileptic seizures (PNES) are involuntary paroxysmal events that are unaccompanied by epileptiform EEG discharges. We hypothesised that PNES are a disorder of distributed brain networks resulting from their functional disconnection.The disconnection may underlie a dissociation mechanism that weakens the influence of unconsciously presented traumatising information but exerts maladaptive effects leading to episodic failures of behavioural control manifested by psychogenic 'seizures'. METHODS: To test this hypothesis, we compared functional connectivity (FC) derived from resting state high-density EEGs of 18 patients with PNES and 18 age-matched and gender-matched controls. To this end, the EEGs were transformed into source space using the local autoregressive average inverse solution. FC was estimated with a multivariate measure of lagged synchronisation in the θ, α and ß frequency bands for 66 brain sites clustered into 18 regions. A multiple comparison permutation test was applied to deduce significant between-group differences in inter-regional and intraregional FC. RESULTS: The significant effect of PNES-a decrease in lagged FC between the basal ganglia and limbic, prefrontal, temporal, parietal and occipital regions-was found in the α band. CONCLUSION: We believe that this finding reveals a possible neurobiological substrate of PNES, which explains both attenuation of the effect of potentially disturbing mental representations and the occurrence of PNES episodes. By improving understanding of the aetiology of this condition, our results suggest a potential refinement of diagnostic criteria and management principles.

Synchronization of EEG: bivariate and multivariate measures.

Synchronization behavior of electroencephalographic (EEG) signals is important for decoding information processing in the human brain. Modern multichannel EEG allows a transition from traditional measurements of synchronization in pairs of EEG signals to whole-brain synchronization maps. The latter can be based on bivariate measures (BM) via averaging over pair-wise values or, alternatively, on multivariate measures (MM), which directly ascribe a single value to the synchronization in a group. In order to compare BM versus MM, we applied nine different estimators to simulated multivariate time series with known parameters and to real EEGs.We found widespread correlations between BM and MM, which were almost frequency-independent for all the measures except coherence. The analysis of the behavior of synchronization measures in simulated settings with variable coupling strength, connection probability, and parameter mismatch showed that some of them, including S-estimator, S-Renyi, omega, and coherence, aremore sensitive to linear interdependences,while others, like mutual information and phase locking value, are more responsive to nonlinear effects. Onemust consider these properties together with the fact thatMM are computationally less expensive and, therefore, more efficient for the large-scale data sets than BM while choosing a synchronization measure for EEG analysis.

Properties of functional brain networks correlate with frequency of psychogenic non-epileptic seizures.

Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness (SW) metrics, respectively. Yet the number of PNES attacks per month correlated with a weakness of local connectedness and a skewed balance between local and global connectedness quantified with SW, all in EEG alpha band. In beta band, patients demonstrated above-normal resiliency, measured with assortativity coefficient, which also correlated with the frequency of PNES attacks. This interictal EEG phenotype may help improve differentiation between PNES and epilepsy. The results also suggest that local connectivity could be a target for therapeutic interventions in PNES. Selective modulation (strengthening) of local connectivity might improve the skewed balance between local and global connectivity and so prevent PNES events.