Cross-frequency power coupling between hierarchically organized face-selective areas

Developmental Differences in Neuromagnetic Cortical Activation and Phase Synchrony Elicited by Scenes with Faces during Movie Watching

The neural underpinnings of humans’ ability to process faces and how it changes over typical development have been extensively studied using paradigms where face stimuli are oversimplified, isolated, and decontextualized. The prevalence of this approach, however, has resulted in limited knowledge of face processing in ecologically valid situations, in which faces are accompanied by contextual information at multiple time scales. In the present study, we use a naturalistic movie paradigm to investigate how neuromagnetic activation and phase synchronization elicited by faces from movie scenes in humans differ between children and adults. We used MEG data from 22 adults (6 females, 3 left handed; mean age, 27.7 ± 5.28 years) and 20 children (7 females, 1 left handed; mean age, 9.5 ± 1.52 years) collected during movie viewing. We investigated neuromagnetic time-locked activation and phase synchronization elicited by movie scenes containing faces in contrast to other movie scenes. Statistical differences between groups were tested using a multivariate data-driven approach. Our results revealed lower face-elicited activation and theta/alpha phase synchrony between 120 and 330 ms in children compared with adults. Reduced connectivity in children was observed between the primary visual areas as well as their connections with higher-order frontal and parietal cortical areas. This is the first study to map neuromagnetic developmental changes in face processing in a time-locked manner using a naturalistic movie paradigm. It supports and extends the existing evidence of core face-processing network maturation accompanied by the development of an extended system of higher-order cortical areas engaged in face processing.

Spectral fingerprints of facial affect processing bias in major depression disorder

In major depressive disorder (MDD), processing of facial affect is thought to reflect a perceptual bias (toward negative emotion, away from positive emotion, and interpretation of neutral as emotional). However, it is unclear to what extent and which specific perceptual bias is represented in MDD at the behavior and neuronal level. The present report examined 48 medication naive MDD patients and 41 healthy controls (HCs) performing a facial affect judgment task while magnetoencephalography was recorded. MDD patients were characterized by overall slower response times and lower perceptual judgment accuracies. In comparison with HC, MDD patients exhibited less somatosensory beta activity (20–30 Hz) suppression, more visual gamma activity (40–80 Hz) modulation and somatosensory beta and visual gamma interaction deficit. Moreover, frontal gamma activity during positive facial expression judgment was found to be negatively correlated with depression severity. Present findings suggest that perceptual bias in MDD is associated with distinct spatio-spectral manifestations on the neural level, which potentially establishes aberrant pathways during facial emotion processing and contributes to MDD pathology.

Co-localization of theta-band activity and hemodynamic responses during face perception: simultaneous electroencephalography and functional near-infrared spectroscopy recordings

Face-specific neural processes in the human brain have been localized to multiple anatomical structures and associated with diverse and dynamic social functions. The question of how various face-related systems and functions may be bound together remains an active area of investigation. We hypothesize that face processing may be associated with specific frequency band oscillations that serve to integrate distributed face processing systems. Using a multimodal imaging approach, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), simultaneous signals were acquired during face and object picture viewing. As expected for face processing, hemodynamic activity in the right occipital face area (OFA) increased during face viewing compared to object viewing, and in a subset of participants, the expected N170 EEG response was observed for faces. Based on recently reported associations between the theta band and visual processing, we hypothesized that increased hemodynamic activity in a face processing area would also be associated with greater theta-band activity originating in the same area. Consistent with our hypothesis, theta-band oscillations were also localized to the right OFA for faces, whereas alpha- and beta-band oscillations were not. Together, these findings suggest that theta-band oscillations originating in the OFA may be part of the distributed face-specific processing mechanism.

Signature Patterns for Top-Down and Bottom-Up Information Processing via Cross-Frequency Coupling in Macaque Auditory Cortex

Predictive coding is a theoretical framework that provides a functional interpretation of top-down and bottom-up interactions in sensory processing. The theory suggests there are differences in message passing up versus down the cortical hierarchy. These differences result from the linear feedforward of prediction errors, and the nonlinear feedback of predictions. This implies that cross-frequency interactions should predominate top-down. But it remains unknown whether these differences are expressed in cross-frequency interactions in the brain. Here we examined bidirectional cross-frequency coupling across four sectors of the auditory hierarchy in the macaque. We computed two measures of cross-frequency coupling, phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC). Our findings revealed distinct patterns for bottom-up and top-down information processing among cross-frequency interactions. Both top-down and bottom-up interactions made prominent use of low frequencies: low-to-low-frequency (theta, alpha, beta) and low-frequency-to-high- gamma couplings were predominant top-down, while low-frequency-to-low-gamma couplings were predominant bottom-up. These patterns were largely preserved across coupling types (PAC and AAC) and across stimulus types (natural and synthetic auditory stimuli), suggesting that they are a general feature of information processing in auditory cortex. Our findings suggest the modulatory effect of low frequencies on gamma-rhythms in distant regions is important for bidirectional information transfer. The finding of low-frequency-to-low-gamma interactions in the bottom-up direction suggest that nonlinearities may also play a role in feedforward message passing. Altogether, the patterns of cross-frequency interaction we observed across the auditory hierarchy are largely consistent with the predictive coding framework.

Propofol-induced unresponsiveness is associated with impaired feedforward connectivity in cortical hierarchy

BACKGROUND

Impaired consciousness has been associated with impaired cortical signal propagation after transcranial magnetic stimulation (TMS). We hypothesised that the reduced current propagation under propofol-induced unresponsiveness is associated with changes in both feedforward and feedback connectivity across the cortical hierarchy.

METHODS

Eight subjects underwent left occipital TMS coupled with high-density EEG recordings during wakefulness and propofol-induced unconsciousness. Spectral analysis was applied to responses recorded from sensors overlying six hierarchical cortical sources involved in visual processing. Dynamic causal modelling (DCM) of induced time-frequency responses and evoked response potentials were used to investigate propofol's effects on connectivity between regions.

RESULTS

Sensor space analysis demonstrated that propofol reduced both induced and evoked power after TMS in occipital, parietal, and frontal electrodes. Bayesian model selection supported a DCM with hierarchical feedforward and feedback connections. DCM of induced EEG responses revealed that the primary effect of propofol was impaired feedforward responses in cross-frequency theta/alpha-gamma coupling and within frequency theta coupling (F contrast, family-wise error corrected P<0.05). An exploratory analysis (thresholded at uncorrected P<0.001) also suggested that propofol impaired feedforward and feedback beta band coupling. Post hoc analyses showed impairments in all feedforward connections and one feedback connection from parietal to occipital cortex. DCM of the evoked response potential showed impaired feedforward connectivity between left-sided occipital and parietal cortex (T contrast P=0.004, Bonferroni corrected).

CONCLUSIONS

Propofol-induced loss of consciousness is associated with impaired hierarchical feedforward connectivity assessed by EEG after occipital TMS.

Modulation of Neural Oscillatory Activity during Dynamic Face Processing

Various neuroimaging and neurophysiological methods have been used to examine neural activation patterns in response to faces. However, much of previous research has relied on static images of faces, which do not allow a complete description of the temporal structure of face-specific neural activities to be made. More recently, insights are emerging from fMRI studies about the neural substrates that underpin our perception of naturalistic dynamic face stimuli, but the temporal and spectral oscillatory activity associated with processing dynamic faces has yet to be fully characterized. Here, we used MEG and beamformer source localization to examine the spatiotemporal profile of neurophysiological oscillatory activity in response to dynamic faces. Source analysis revealed a number of regions showing enhanced activation in response to dynamic relative to static faces in the distributed face network, which were spatially coincident with regions that were previously identified with fMRI. Furthermore, our results demonstrate that perception of realistic dynamic facial stimuli activates a distributed neural network at varying time points facilitated by modulations in low-frequency power within alpha and beta frequency ranges (8-30 Hz). Naturalistic dynamic face stimuli may provide a better means of representing the complex nature of perceiving facial expressions in the real world, and neural oscillatory activity can provide additional insights into the associated neural processes.

Brain synchronization during perception of facial emotional expressions with natural and unnatural dynamics

Research on the perception of facial emotional expressions (FEEs) often uses static images that do not capture the dynamic character of social coordination in natural settings. Recent behavioral and neuroimaging studies suggest that dynamic FEEs (videos or morphs) enhance emotion perception. To identify mechanisms associated with the perception of FEEs with natural dynamics, the present EEG (Electroencephalography)study compared (i) ecologically valid stimuli of angry and happy FEEs with natural dynamics to (ii) FEEs with unnatural dynamics, and to (iii) static FEEs. FEEs with unnatural dynamics showed faces moving in a biologically possible but unpredictable and atypical manner, generally resulting in ambivalent emotional content. Participants were asked to explicitly recognize FEEs. Using whole power (WP) and phase synchrony (Phase Locking Index, PLI), we found that brain responses discriminated between natural and unnatural FEEs (both static and dynamic). Differences were primarily observed in the timing and brain topographies of delta and theta PLI and WP, and in alpha and beta WP. Our results support the view that biologically plausible, albeit atypical, FEEs are processed by the brain by different mechanisms than natural FEEs. We conclude that natural movement dynamics are essential for the perception of FEEs and the associated brain processes.

EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity

Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency) network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG) datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation). In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT) score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

Low-frequency oscillations employ a general coding of the spatio-temporal similarity of dynamic faces

Brain networks use neural oscillations as information transfer mechanisms. Although the face perception network in occipitotemporal cortex is well-studied, contributions of oscillations to face representation remain an open question. We tested for links between oscillatory responses that encode facial dimensions and the theoretical proposal that faces are encoded in similarity-based "face spaces". We quantified similarity-based encoding of dynamic faces in magnetoencephalographic sensor-level oscillatory power for identity, expression, physical and perceptual similarity of facial form and motion. Our data show that evoked responses manifest physical and perceptual form similarity that distinguishes facial identities. Low-frequency induced oscillations (< 20Hz) manifested more general similarity structure, which was not limited to identity, and spanned physical and perceived form and motion. A supplementary fMRI-constrained source reconstruction implicated fusiform gyrus and V5 in this similarity-based representation. These findings introduce a potential link between "face space" encoding and oscillatory network communication, which generates new hypotheses about the potential oscillation-mediated mechanisms that might encode facial dimensions.

Bidirectional electric communication between the inferior occipital gyrus and the amygdala during face processing

Faces contain multifaceted information that is important for human communication. Neuroimaging studies have revealed face-specific activation in multiple brain regions, including the inferior occipital gyrus (IOG) and amygdala; it is often assumed that these regions constitute the neural network responsible for the processing of faces. However, it remains unknown whether and how these brain regions transmit information during face processing. This study investigated these questions by applying dynamic causal modeling of induced responses to human intracranial electroencephalography data recorded from the IOG and amygdala during the observation of faces, mosaics, and houses in upright and inverted orientations. Model comparisons assessing the experimental effects of upright faces versus upright houses and upright faces versus upright mosaics consistently indicated that the model having face-specific bidirectional modulatory effects between the IOG and amygdala was the most probable. The experimental effect between upright versus inverted faces also favored the model with bidirectional modulatory effects between the IOG and amygdala. The spectral profiles of modulatory effects revealed both same-frequency (e.g., gamma-gamma) and cross-frequency (e.g., theta-gamma) couplings. These results suggest that the IOG and amygdala communicate rapidly with each other using various types of oscillations for the efficient processing of faces. Hum Brain Mapp 38:4511-4524, 2017. © 2017 Wiley Periodicals, Inc.

Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies. Considering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.

Causal Interactions between Frontal(θ) - Parieto-Occipital(α2) Predict Performance on a Mental Arithmetic Task

Many neuroimaging studies have demonstrated the different functional contributions of spatially distinct brain areas to working memory (WM) subsystems in cognitive tasks that demand both local information processing and interregional coordination. In WM cognitive task paradigms employing electroencephalography (EEG), brain rhythms such as θ and α have been linked to specific functional roles over given brain areas, but their functional coupling has not been extensively studied. Here we analyzed an arithmetic task with five cognitive workload levels (CWLs) and demonstrated functional/effective coupling between the two WM subsystems: the central executive located over frontal (F) brain areas that oscillates on the dominant θ rhythm (Frontal^θ/F^θ) and the storage buffer located over parieto-occipital (PO) brain areas that operates on the α₂ dominant brain rhythm (Parieto-Occipital^α2/PO^α2). We focused on important differences between and within WM subsystems in relation to behavioral performance. A repertoire of brain connectivity estimators was employed to elucidate the distinct roles of amplitude, phase within and between frequencies, and the hierarchical role of functionally specialized brain areas related to the task. Specifically, for each CWL, we conducted a) a conventional signal power analysis within both frequency bands at F^θ and PO^α2, b) the intra- and inter-frequency phase interactions between F^θ and PO^α2, and c) their causal phase and amplitude relationship. We found no significant statistical difference of signal power or phase interactions between correct and wrong answers. Interestingly, the study of causal interactions between F^θ and PO^α2 revealed frontal brain region(s) as the leader, while the strength differentiated between correct and wrong responses in every CWL with absolute accuracy. Additionally, zero time-lag between bilateral F^θ and right PO^a2 could serve as an indicator of mental calculation failure. Overall, our study highlights the significant role of coordinated activity between F^θ and PO^α2 via their causal interactions and the timing for arithmetic performance.

Effective Connectivity from Early Visual Cortex to Posterior Occipitotemporal Face Areas Supports Face Selectivity and Predicts Developmental Prosopagnosia

Face processing is mediated by interactions between functional areas in the occipital and temporal lobe, and the fusiform face area (FFA) and anterior temporal lobe play key roles in the recognition of facial identity. Individuals with developmental prosopagnosia (DP), a lifelong face recognition impairment, have been shown to have structural and functional neuronal alterations in these areas. The present study investigated how face selectivity is generated in participants with normal face processing, and how functional abnormalities associated with DP, arise as a function of network connectivity. Using functional magnetic resonance imaging and dynamic causal modeling, we examined effective connectivity in normal participants by assessing network models that include early visual cortex (EVC) and face-selective areas and then investigated the integrity of this connectivity in participants with DP. Results showed that a feedforward architecture from EVC to the occipital face area, EVC to FFA, and EVC to posterior superior temporal sulcus (pSTS) best explained how face selectivity arises in both controls and participants with DP. In this architecture, the DP group showed reduced connection strengths on feedforward connections carrying face information from EVC to FFA and EVC to pSTS. These altered network dynamics in DP contribute to the diminished face selectivity in the posterior occipitotemporal areas affected in DP. These findings suggest a novel view on the relevance of feedforward projection from EVC to posterior occipitotemporal face areas in generating cortical face selectivity and differences in face recognition ability.

SIGNIFICANCE STATEMENT

Areas of the human brain showing enhanced activation to faces compared to other objects or places have been extensively studied. However, the factors leading to this face selectively have remained mostly unknown. We show that effective connectivity from early visual cortex to posterior occipitotemporal face areas gives rise to face selectivity. Furthermore, people with developmental prosopagnosia, a lifelong face recognition impairment, have reduced face selectivity in the posterior occipitotemporal face areas and left anterior temporal lobe. We show that this reduced face selectivity can be predicted by effective connectivity from early visual cortex to posterior occipitotemporal face areas. This study presents the first network-based account of how face selectivity arises in the human brain.

A multi-layer network approach to MEG connectivity analysis

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.

Time-varying effective connectivity during visual object naming as a function of semantic demands

Accumulating evidence suggests that visual object understanding involves a rapid feedforward sweep, after which subsequent recurrent interactions are necessary. The extent to which recurrence plays a critical role in object processing remains to be determined. Recent studies have demonstrated that recurrent processing is modulated by increasing semantic demands. Differentially from previous studies, we used dynamic causal modeling to model neural activity recorded with magnetoencephalography while 14 healthy humans named two sets of visual objects that differed in the degree of semantic accessing demands, operationalized in terms of the values of basic psycholinguistic variables associated with the presented objects (age of acquisition, frequency, and familiarity). This approach allowed us to estimate the directionality of the causal interactions among brain regions and their associated connectivity strengths. Furthermore, to understand the dynamic nature of connectivity (i.e., the chronnectome; Calhoun et al., 2014) we explored the time-dependent changes of effective connectivity during a period (200-400 ms) where adding semantic-feature information improves modeling and classifying visual objects, at 50 ms increments. First, we observed a graded involvement of backward connections, that became active beyond 200 ms. Second, we found that semantic demands caused a suppressive effect in the backward connection from inferior frontal cortex (IFC) to occipitotemporal cortex over time. These results complement those from previous studies underscoring the role of IFC as a common source of top-down modulation, which drives recurrent interactions with more posterior regions during visual object recognition. Crucially, our study revealed the inhibitory modulation of this interaction in situations that place greater demands on the conceptual system.

Structural and effective connectivity reveals potential network-based influences on category-sensitive visual areas

Visual category perception is thought to depend on brain areas that respond specifically when certain categories are viewed. These category-sensitive areas are often assumed to be “modules” (with some degree of processing autonomy) and to act predominantly on feedforward visual input. This modular view can be complemented by a view that treats brain areas as elements within more complex networks and as influenced by network properties. This network-oriented viewpoint is emerging from studies using either diffusion tensor imaging to map structural connections or effective connectivity analyses to measure how their functional responses influence each other. This literature motivates several hypotheses that predict category-sensitive activity based on network properties. Large, long-range fiber bundles such as inferior fronto-occipital, arcuate and inferior longitudinal fasciculi are associated with behavioral recognition and could play crucial roles in conveying backward influences on visual cortex from anterior temporal and frontal areas. Such backward influences could support top-down functions such as visual search and emotion-based visual modulation. Within visual cortex itself, areas sensitive to different categories appear well-connected (e.g., face areas connect to object- and motion sensitive areas) and their responses can be predicted by backward modulation. Evidence supporting these propositions remains incomplete and underscores the need for better integration of DTI and functional imaging.

The neural mechanism underlying the effects of preceding contexts on current categorization decisions

Preceding contexts strongly influence current decision-making. To elucidate the neural mechanism that underlies this phenomenon, magnetoencephalographic signals were recorded while participants performed a binary categorization task on a sequence of facial expressions. The behavioral data indicated that the categorization of current facial expressions differed between the contexts shaped by the immediately preceding expression. We found that the effects of the preceding context were linked to prestimulus power activities in the low-frequency band. However, these context-dependent neural markers did not reflect behavioral decisions. Rather, the beta power observed primarily after stimulus onset and located at distinct sensors was predictive of the trial-by-trial decisions. Despite these results, the coupling strength between context-dependent and decision-related power differed between preceding contexts, suggesting that the context-dependent power interacted with decision-related power in a systemic manner and in turn biased behavioral decisions. Taken together, these findings suggest that categorization decisions are mediated by a series of power activities that coordinate the influence of preceding contexts on current categorization.

Network Interactions Explain Sensitivity to Dynamic Faces in the Superior Temporal Sulcus

The superior temporal sulcus (STS) in the human and monkey is sensitive to the motion of complex forms such as facial and bodily actions. We used functional magnetic resonance imaging (fMRI) to explore network-level explanations for how the form and motion information in dynamic facial expressions might be combined in the human STS. Ventral occipitotemporal areas selective for facial form were localized in occipital and fusiform face areas (OFA and FFA), and motion sensitivity was localized in the more dorsal temporal area V5. We then tested various connectivity models that modeled communication between the ventral form and dorsal motion pathways. We show that facial form information modulated transmission of motion information from V5 to the STS, and that this face-selective modulation likely originated in OFA. This finding shows that form-selective motion sensitivity in the STS can be explained in terms of modulation of gain control on information flow in the motion pathway, and provides a substantial constraint for theories of the perception of faces and biological motion.

Top-down control of visual responses to fear by the amygdala

The visual cortex is sensitive to emotional stimuli. This sensitivity is typically assumed to arise when amygdala modulates visual cortex via backwards connections. Using human fMRI, we compared dynamic causal connectivity models of sensitivity with fearful faces. This model comparison tested whether amygdala modulates distinct cortical areas, depending on dynamic or static face presentation. The ventral temporal fusiform face area showed sensitivity to fearful expressions in static faces. However, for dynamic faces, we found fear sensitivity in dorsal motion-sensitive areas within hMT+/V5 and superior temporal sulcus. The model with the greatest evidence included connections modulated by dynamic and static fear from amygdala to dorsal and ventral temporal areas, respectively. According to this functional architecture, amygdala could enhance encoding of fearful expression movements from video and the form of fearful expressions from static images. The amygdala may therefore optimize visual encoding of socially charged and salient information.