Functional connectivity abnormalities during contextual processing in schizophrenia and in Parkinson's disease

Directed connectivity in Parkinson's disease patients during over-ground and treadmill walking

Treadmill walking is considered a useful therapeutic tool for improving gait in Parkinson's disease (PD) patients. The study investigated the role of top-down, frontal-parietal versus bottom-up parietal-frontal networks, during over-ground and treadmill walking in PD and control subjects, using functional connectivity. To this end, EEG was recorded simultaneously, during a ten-minute period of continuous walking either over-ground or on a treadmill, in thirteen PD patients and thirteen age-matched controls. We evaluated EEG directed connectivity, using phase transfer entropy in three frequency bands: theta, alpha and beta. PD patients showed increased top-down connectivity during over-ground compared with treadmill walking, in the beta frequency range. Control subjects showed no significant differences in connectivity between the two walking conditions. Our results suggest that in PD patients, OG walking was associated with increased allocation of attentional resources, compared with that on the TL. These functional connectivity modulations may shed further light on the mechanisms underlying treadmill versus overground walking in PD.

The architecture of functional brain network modulated by driving during adverse weather conditions

Traffic accidents caused by adverse weather conditions have attracted the attention of many countries. Previous studies have focused on the driver's response in a particular situation under foggy conditions, but little is known about the functional brain network (FBN) topology that is modulated by driving in foggy weather, especially when the vehicle encounters cars in the opposite lane. An experiment consisting of two driving tasks is designed and conducted using sixteen participants. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase-locking value (PLV). Based on this, a PLV-weighted network is subsequently generated. The clustering coefficient (C) and the characteristic path length (L) are adopted as measures for the graph analysis. Statistical analyses are performed on graph-derived metrics. The major finding is that the PLV is significantly increased in the delta, theta and beta frequency bands while driving in foggy weather. Additionally, for the brain network topology metric, compared with driving in clear weather, significant increases are observed (driving in foggy weather) in the clustering coefficient for alpha and beta frequency bands and the characteristic path length for all frequency bands considered in this work. Driving in foggy weather would regulate FBN reorganization in different frequency bands. Our findings also suggest that the effects of adverse weather conditions on functional brain networks with a trend toward a more economic but less efficient architecture. Graph theory analysis may be a beneficial tool to further understand the neural mechanisms of driving in adverse weather conditions, which in turn may help to reduce the occurrence of road traffic accidents to some extent.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09825-y.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

Brain Connectivity and Graph Theory Analysis in Alzheimer’s and Parkinson’s Disease: The Contribution of Electrophysiological Techniques

In recent years, applications of the network science to electrophysiological data have increased as electrophysiological techniques are not only relatively low cost, largely available on the territory and non-invasive, but also potential tools for large population screening. One of the emergent methods for the study of functional connectivity in electrophysiological recordings is graph theory: it allows to describe the brain through a mathematic model, the graph, and provides a simple representation of a complex system. As Alzheimer’s and Parkinson’s disease are associated with synaptic disruptions and changes in the strength of functional connectivity, they can be well described by functional connectivity analysis computed via graph theory. The aim of the present review is to provide an overview of the most recent applications of the graph theory to electrophysiological data in the two by far most frequent neurodegenerative disorders, Alzheimer’s and Parkinson’s diseases.

Altered directed connectivity during processing of predictive stimuli in psychiatric patient populations

OBJECTIVES

The study investigated the role of top-down versus bottom-up connectivity, during the processing of predictive information, in three different psychiatric disorders.

METHODS

Electroencephalography (EEG) was recorded during the performance of a task, which evaluates the ability to use predictive information in order to facilitate predictable versus random target detection. We evaluated EEG event-related directed connectivity, in patients with schizophrenia (SZ), major depressive disorder (MDD), and autism spectrum disorder (ASD), compared with healthy age-matched controls. Directed connectivity was evaluated using phase transfer entropy.

RESULTS

We showed that top-down frontal-parietal connectivity was weaker in SZ (theta and beta bands) and ASD (alpha band) compared to control subjects, during the processing of stimuli consisting of the predictive sequence. In SZ patients, top-down connectivity was also attenuated, during the processing of predictive targets in the beta frequency band. In contrast, compared with controls, MDD patients displayed an increased top-down flow of information, during the processing of predicted targets (alpha band).

CONCLUSIONS

The findings suggest that top-down frontal-parietal connectivity is altered differentially across three major psychiatric disorders, specifically during the processing of predictive stimuli.

SIGNIFICANCE

Altered top-down connectivity may contribute to the specific prediction deficits observed in each of the patient populations.

The effect of photic stimulation alone and in combination with sleep deprivation after a seizure-like event - reappraisal by using linear and nonlinear EEG methods

ObjectivesThe present study aimed to compare the effectiveness of different provocation tests used for the study of the 'susceptibility to seizure' by quantitative electroencephalography (EEG) analysis.MethodsEight subjects with a history of a seizure-like disturbed consciousness participated in this preliminary study. A routine EEG was carried out with photic stimulation (eyes closed and after eyes open) at the beginning of the investigation. Some days later, a sleep-deprived EEG was recorded with the same protocol. Selected epochs (in eyes closed condition) after the stimulations were analysed with Point(wise) Correlation Dimension (PD2i) and Synchronization Likelihood (SL) methods. The results were compared to those obtained by similar analysis of the resting state (control) epochs with Wilcoxon Signed Rank Test (p ≤ 0.05).ResultsIn our study, significantly lower grand mean PD2i and higher delta SL values were found in sleep-deprived state after stimulation with eyes closed compared to the control. Our results indicated a lower-dimensional, hypersynchronous state of the brain as a consequence of these combined provocations.DiscussionThis may correspond to a possible 'preictal' state of the brain. Accordingly, it is suggested that photic stimulation together with sleep deprivation seems to be more effective in provocation - especially when the stimulation was made with eyes closed.

Altered directed connectivity during processing of implicit versus explicit predictive stimuli in Parkinson's disease patients

The study investigated the role of top-down versus bottom-up connectivity, during the processing of implicit or explicit predictive information, in Parkinson's disease (PD). EEG was recorded during the performance of a task, which evaluated the ability to utilize either implicit or explicit predictive contextual information in order to facilitate the detection of predictable versus random targets. Thus, subjects performed an implicit and explicit session, where subjects were either unaware or made aware of a predictive sequence that signals the presentation of a subsequent target, respectively. We evaluated EEG event-related directed connectivity, in PD patients compared with healthy age-matched controls, using phase transfer entropy. PD patients showed increased top-down frontal-parietal connectivity, compared to control subjects, during the processing of the last (most informative) stimulus of the predictive sequence and of random standards, in the implicit and explicit session, respectively. These findings suggest that PD is associated with compensatory top-down connectivity, specifically during the processing of implicit predictive stimuli. During the explicit session, PD patients seem to allocate more attentional resources to non-informative standard stimuli, compared to controls. These connectivity changes shed further light on the cognitive deficits, associated with the processing of predictive contextual information, that are observed in PD patients.

Contextual Processing and the Impacts of Aging and Neurodegeneration: A Scoping Review

Contextual processing (or context processing; CP) is an integral component of cognition. CP allows people to manage their thoughts and actions by adjusting to surroundings. CP involves the formation of an internal representation of context in relation to the environment, maintenance of this information over a period of time, and the updating of mental representations to reflect changes in the environment. Each of these functions can be affected by aging and associated conditions. Here, we introduced contextual processing research and summarized the literature studying the impact of normal aging and neurodegeneration-related cognitive decline on CP. Through searching the PubMed, PsycINFO, and Google Scholar databases, 23 studies were retrieved that focused on the impact of aging, mild cogniitve impairment (MCI), Alzheimer's disease (AD), and Parkinson's disease (PD) on CP. Results indicated that CP is particularly vulnerable to aging and neurodegeneration. Older adults had a delayed onset and reduced amplitude of electrophysiological response to information detection, comparison, and execution. MCI patients demonstrated clear signs of impaired CP compared to normal aging. The only study on AD suggested a decreased proactive control in AD participants in maintaining contextual information, but seemingly intact reactive control. Studies on PD restricted to non-demented older participants, who showed limited ability to use contextual information in cognitive and motor processes, exhibiting impaired reactive control but more or less intact proactive control. These data suggest that the decline in CP with age is further impacted by accelerated aging and neurodegeneration, providing insights for improving intervention strategies. This review highlights the need for increased attention to research this important but understudied field.

Demystifying signal processing techniques to extract resting-state EEG features for psychologists

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia

Previous studies have reported that schizophrenia (SZ) patients showed selective reinforcement learning deficits and abnormal feedback-related event-related potential (ERP) components. However, how the brain networks and their topological properties evolve over time during transient feedback-related cognition processing in SZ patients has not been investigated so far. In this paper, using publicly available feedback-related ERP data which were recorded from SZ patients and healthy controls (HC) when they performed a reinforcement learning task, we carried out an event-related network analysis where topology of brain functional networks was characterized with some graph measures including clustering coefficient (C), global efficiency (E_global) and local efficiency (E_local) on a millisecond timescale. Our results showed that the brain functional networks displayed rapid rearrangements of topological properties during transient feedback-related cognition process for both two groups. More importantly, we found that SZ patients exhibited significantly reduced theta-band (time window of 170-350 ms after stimuli onset) brain functional connectivity strength, E_global, E_local and C in response to negative feedback stimuli compared to HC group. The network based statistic (NBS) analysis detected one significantly decreased theta-band subnetwork in SZ patients mainly involving in frontal-occipital and temporal-occipital connections compared to HC group. In addition, clozapine treatment seemed to greatly reduce theta-band power and topological measures of brain networks in SZ patients. Finally, the theta-band power, graph measures and functional connectivity were extracted to train a support vector machine classifier for classification of HC from SZ, or Cloz + SZ or Cloz- SZ, and a relatively good classification accuracy of 84.48%, 89.47% and 78.26% was obtained, respectively. The above results suggested a less optimal organization of theta-band brain network in SZ patients, and studying the topological parameters of brain networks evolve over time during transient feedback-related processing could be useful for understanding the pathophysiologic mechanisms underlying reinforcement learning deficits in SZ patients.

Hyper-Activated Brain Resting-State Network and Mismatch Negativity Deficit in Schizophrenia With Auditory Verbal Hallucination Revealed by an Event-Related Potential Evidence

Schizophrenia is a holergasia with unclear mechanism and high heterogeneity. Auditory verbal hallucination (AVH) study might help in understanding schizophrenia from the perspective of individual symptoms. This study aimed to investigate the activities of the resting-state networks (RSN) in the electroencephalogram (EEG) and mismatch negativity (MMN) in task-related state of schizophrenia patients with AVH. We recruited 30 schizophrenia patients without any medication for more than 4 weeks (15 AVH patients and 15 Non-AVH patients) and 15 healthy controls. We recorded the EEG data of the participants in the resting-state for 7 min and the event-related potential (ERP) data under an auditory oddball paradigm. In the resting-state EEG network, AVH patients exhibited a higher clustering coefficient than Non-AVH patients and healthy controls on delta and beta bands and a shorter characteristic path length than Non-AVH patients and healthy controls on all frequency bands. For ERP data, AVH patients showed a lower MMN amplitude than healthy controls (p = 0.017) and Non-AVH patients (p = 0.033). What's more, MMN amplitude was positively correlated with clustering coefficient, and negatively correlated with characteristic path length on delta, theta, beta and gamma band in AVH patients. Our results indicate that AVH patients showed a hyper-activity in resting-state and may have impaired higher-order auditory expectations in the task-related state than healthy controls and Non-AVH patients. And it seems reasonable to conclude that the formation of AVH may occupy certain brain resources and compete for brain resources with external auditory stimuli.

Altered Functional Connectivity of Alpha Rhythm in Obsessive-Compulsive Disorder During Rest

Obsessive-compulsive disorder (OCD) is a common inheritable psychiatric disorder characteristic of repetitive thinking, imagination (obsession), and stereotyped behaviors (compulsive). To explore whether there is an alteration of brain functional connectivity (BFC) in patients with OCD during rest, electroencephalogram (EEG) data of healthy controls (HCs) and patients with OCD were collected during rest in both eyes-closed and eyes-open states. Synchronization likelihood and graph theory were applied to construct and analyze brain functional networks of patients with OCD and HCs. Patients with OCD showed abnormal graph-theoretic parameters and impaired small world features in the alpha and beta bands. In addition, the topological analysis consistently showed that the long-range BFC of alpha rhythm was significantly reduced in the bilateral posterior areas in patients with OCD in comparison with HCs, while the BFC in the beta rhythm was significantly increased only in the eyes-open state. The findings suggest that the BFC of patients with OCD show abnormal small-world features and altered topological structure during rest, mainly in alpha and beta bands, which may provide a new insight for the diagnosis and treatment of OCD.

Attribution of intentions and context processing in psychometric schizotypy

INTRODUCTION

Impairment in Theory of mind (TOM) has frequently been associated with schizophrenia and with schizotypy. Studies have found that a tendency to over-attribute intentions and special meaning to events and to people is related to positive psychotic symptoms. Further, it has been suggested that this intentionality bias may be due to a broader deficit in context processing (CP). The aim of the present study was thus to investigate the relationship between positive schizotypy and both over-attribution of intentions and contextual processing.

METHODS

One-hundred and nineteen healthy individuals completed the Schizotypal Personality Questionnaire and were assessed with tasks measuring contextual treatment and ToM.

RESULTS

Results revealed that positive schizotypy was significantly related to an over-attribution of intentions on the ToM task and with a faster processing of implicit context. Partial correlational analyses indicated that the association between the attribution of intentions and positive schizotypy was not explained by a deficit of CP. In contrast, stepwise multiple regression analyses showed that both an over-attribution of intentions and a faster processing of implicit context significantly predicted positive schizotypy.

CONCLUSIONS

These results show that an over-attribution of intention is independent from a broader deficit in context information processing and that they both possibly contribute to the development and maintenance of positive psychotic symptoms.

Altered Small-World Networks in First-Episode Schizophrenia Patients during Cool Executive Function Task

At present, little is known about brain functional connectivity and its small-world topologic properties in first-episode schizophrenia (SZ) patients during cool executive function task. In this paper, the Trail Making Test-B (TMT-B) task was used to evaluate the cool executive function of first-episode SZ patients and electroencephalography (EEG) data were recorded from 14 first-episode SZ patients and 14 healthy controls during this cool executive function task. Brain functional connectivity between all pairs of EEG channels was constructed based on mutual information (MI) analysis. The constructed brain functional networks were filtered by three thresholding schemes: absolute threshold, mean degree, and a novel data-driven scheme based on orthogonal minimal spanning trees (OMST), and graph theory was then used to study the topographical characteristics of the filtered brain graphs. Results indicated that the graph theoretical measures of the theta band showed obvious difference between SZ patients and healthy controls. In the theta band, the characteristic path length was significantly longer and the cluster coefficient was significantly smaller in the SZ patients for a wide range of absolute threshold T. However, the cluster coefficient showed no significant changes, and the characteristic path length was still significantly longer in SZ patients when calculated as a function of mean degree K. Interestingly, we also found that only the characteristic path length was significantly longer in SZ patients compared with healthy controls after using the OMST scheme. Pearson correlation analysis showed that the characteristic path length was positively correlated with executive time of TMT-B for the combined SZ patients and healthy controls (r = 0.507, P = 0.006), but not for SZ patients alone (r = 0.072, P = 0.612). The above results suggested a less optimal organization of the brain network and could be useful for understanding the pathophysiologic mechanisms underlying cool executive dysfunction in first-episode SZ patients.

Social neuroscience: undoing the schism between neurology and psychiatry

Multiple disorders once jointly conceived as "nervous diseases" became segregated by the distinct institutional traditions forged in neurology and psychiatry. As a result, each field specialized in the study and treatment of a subset of such conditions. Here we propose new avenues for interdisciplinary interaction through a triangulation of both fields with social neuroscience. To this end, we review evidence from five relevant domains (facial emotion recognition, empathy, theory of mind, moral cognition, and social context assessment), highlighting their common disturbances across neurological and psychiatric conditions and discussing their multiple pathophysiological mechanisms. Our proposal is anchored in multidimensional evidence, including behavioral, neurocognitive, and genetic findings. From a clinical perspective, this work paves the way for dimensional and transdiagnostic approaches, new pharmacological treatments, and educational innovations rooted in a combined neuropsychiatric training. Research-wise, it fosters new models of the social brain and a novel platform to explore the interplay of cognitive and social functions. Finally, we identify new challenges for this synergistic framework.

How Different EEG References Influence Sensor Level Functional Connectivity Graphs

Highlights: Hamming Distance is applied to distinguish the difference of functional connectivity networkThe orientations of sources are testified to influence the scalp Functional Connectivity Graph (FCG) from different references significantlyREST, the reference electrode standardization technique, is proved to have an overall stable and excellent performance in variable situations. The choice of an electroencephalograph (EEG) reference is a practical issue for the study of brain functional connectivity. To study how EEG reference influence functional connectivity estimation (FCE), this study compares the differences of FCE resulting from the different references such as REST (the reference electrode standardization technique), average reference (AR), linked mastoids (LM), and left mastoid references (LR). Simulations involve two parts. One is based on 300 dipolar pairs, which are located on the superficial cortex with a radial source direction. The other part is based on 20 dipolar pairs. In each pair, the dipoles have various orientation combinations. The relative error (RE) and Hamming distance (HD) between functional connectivity matrices of ideal recordings and that of recordings obtained with different references, are metrics to compare the differences of the scalp functional connectivity graph (FCG) derived from those two kinds of recordings. Lower RE and HD values imply more similarity between the two FCGs. Using the ideal recording (IR) as a standard, the results show that AR, LM and LR perform well only in specific conditions, i.e., AR performs stable when there is no upward component in sources' orientation. LR achieves desirable results when the sources' locations are away from left ear. LM achieves an indistinct difference with IR, i.e., when the distribution of source locations is symmetric along the line linking the two ears. However, REST not only achieves excellent performance for superficial and radial dipolar sources, but also achieves a stable and robust performance with variable source locations and orientations. Benefitting from the stable and robust performance of REST vs. other reference methods, REST might best recover the real FCG of EEG. Thus, REST based FCG may be a good candidate to compare the FCG of EEG based on different references from different labs.

Functional EEG network analysis in schizophrenia: Evidence of larger segregation and deficit of modulation

OBJECTIVE

Higher mental functions depend on global cerebral functional coordination. Our aim was to study fast modulation of functional networks in schizophrenia that has not been previously assessed.

METHODS

Graph-theory was used to analyze the electroencephalographic (EEG) activity during an odd-ball task in 57 schizophrenia patients (18 first episode patients, FEPs) and 59 healthy controls. Clustering coefficient (CLC), characteristic path length (PL) and small-worldness (SW) were computed at baseline ([-300 0] ms prior to stimulus delivery) and response ([150 450] ms post-stimulus) windows. Clinical and cognitive assessments were performed.

RESULTS

CLC, PL and SW showed a significant modulation between baseline and response in controls but not in patients. Patients obtained higher CLC and SW at baseline, lower CLC and higher PL at response, and diminished modulation of CLC and SW as compared to controls. In patients, CLC and SW modulation were inversely associated to cognitive performance in executive tasks and directly associated to working memory. Similar patterns were observed in FEPs. CLC and SW during the baseline were inversely associated to their respective modulation magnitudes.

CONCLUSIONS

Our results are coherent with a hyper-segregated network at baseline (higher CLC) and a decreased modulation of the functional connectivity during cognition in schizophrenia.

Non-linear Analysis of Scalp EEG by Using Bispectra: The Effect of the Reference Choice

Bispectral analysis is a signal processing technique that makes it possible to capture the non-linear and non-Gaussian properties of the EEG signals. It has found various applications in EEG research and clinical practice, including the assessment of anesthetic depth, the identification of epileptic seizures, and more recently, the evaluation of non-linear cross-frequency brain functional connectivity. However, the validity and reliability of the indices drawn from bispectral analysis of EEG signals are potentially biased by the use of a non-neutral EEG reference. The present study aims at investigating the effects of the reference choice on the analysis of the non-linear features of EEG signals through bicoherence, as well as on the estimation of cross-frequency EEG connectivity through two different non-linear measures, i.e., the cross-bicoherence and the antisymmetric cross-bicoherence. To this end, four commonly used reference schemes were considered: the vertex electrode (Cz), the digitally linked mastoids, the average reference, and the Reference Electrode Standardization Technique (REST). The reference effects were assessed both in simulations and in a real EEG experiment. The simulations allowed to investigated: (i) the effects of the electrode density on the performance of the above references in the estimation of bispectral measures; and (ii) the effects of the head model accuracy in the performance of the REST. For real data, the EEG signals recorded from 10 subjects during eyes open resting state were examined, and the distortions induced by the reference choice in the patterns of alpha-beta bicoherence, cross-bicoherence, and antisymmetric cross-bicoherence were assessed. The results showed significant differences in the findings depending on the chosen reference, with the REST providing superior performance than all the other references in approximating the ideal neutral reference. In conclusion, this study highlights the importance of considering the effects of the reference choice in the interpretation and comparison of the results of bispectral analysis of scalp EEG.

Aberrant Functional Whole-Brain Network Architecture in Patients With Schizophrenia: A Meta-analysis

Findings from multiple lines of research provide evidence of aberrant functional brain connectivity in schizophrenia. By using graph-analytical measures, recent studies indicate that patients with schizophrenia exhibit changes in the organizational principles of whole-brain networks and that these changes relate to cognitive symptoms. However, there has not been a systematic investigation of functional brain network changes in schizophrenia to test the consistency of these changes across multiple studies. A comprehensive literature search was conducted to identify all available functional graph-analytical studies in patients with schizophrenia. Effect size measures were derived from each study and entered in a random-effects meta-analytical model. All models were tested for effects of potential moderator variables as well as for the presence of publication bias. The results of a total of n = 13 functional neuroimaging studies indicated that brain networks in patients with schizophrenia exhibit significant decreases in measures of local organization (g = -0.56, P = .02) and significant decreases in small-worldness (g = -0.65, P = .01) whereas global short communication paths seemed to be preserved (g = 0.26, P = .32). There was no evidence for a publication bias or moderator effects. The present meta- analysis demonstrates significant changes in whole brain network architecture associated with schizophrenia across studies.

Impact of the reference choice on scalp EEG connectivity estimation

OBJECTIVE

Several scalp EEG functional connectivity studies, mostly clinical, seem to overlook the reference electrode impact. The subsequent interpretation of brain connectivity is thus often biased by the choice of a non-neutral reference. This study aims at systematically investigating these effects.

APPROACH

As EEG reference, we examined the vertex electrode (Cz), the digitally linked mastoids (DLM), the average reference (AVE), and the reference electrode standardization technique (REST). As a connectivity metric, we used the imaginary part of the coherency. We tested simulated and real data (eyes-open resting state) by evaluating the influence of electrode density, the effect of head model accuracy in the REST transformation, and the impact on the characterization of the topology of functional networks from graph analysis.

MAIN RESULTS

Simulations demonstrated that REST significantly reduced the distortion of connectivity patterns when compared to AVE, Cz, and DLM references. Moreover, the availability of high-density EEG systems and an accurate knowledge of the head model are crucial elements to improve REST performance, with the individual realistic head model being preferable to the standard realistic head model. For real data, a systematic change of the spatial pattern of functional connectivity depending on the chosen reference was also observed. The distortion of connectivity patterns was larger for the Cz reference, and progressively decreased when using the DLM, the AVE, and the REST. Strikingly, we also showed that network attributes derived from graph analysis, i.e. node degree and local efficiency, are significantly influenced by the EEG reference choice.

SIGNIFICANCE

Overall, this study highlights that significant differences arise in scalp EEG functional connectivity and graph network properties, in dependence on the chosen reference. We hope that our study will convey the message that caution should be used when interpreting and comparing results obtained from different laboratories using different reference schemes.

Role of the dorsolateral prefrontal cortex in context-dependent motor performance

Context-dependent motor performance is a phenomenon in which people perform better in the environmental context where they originally practised a task. Some animal and computer simulation studies have suggested that context-dependent performance may be associated with neural activation of the dorsolateral prefrontal cortex (DLPFC). This study aimed to determine the role of the DLPFC in context-dependent motor performance by perturbing the neural processing of the DLPFC with repetitive transcranial magnetic stimulation (rTMS) in healthy adults. Thirty healthy adults were recruited into the Control, rTMS DLPFC and rTMS Vertex groups. The participants practised three finger sequences associated with a specific incidental context (a coloured circle and a location on the computer screen). One day following practice, the rTMS groups received 1 Hz rTMS prior to the testing conditions in which the sequence-context associations remained the same as practice (SAME) or changed (SWITCH). All three groups improved significantly over practice on day 1. The second day testing results showed that the DLPFC group had a significantly lower decrease in motor performance under the SWITCH condition than the Control and Vertex groups. This finding suggests a specific role of the DLPFC in context-dependent motor performance.

Neural correlates of local contextual processing across stimulus modalities and patient populations

The objective of the current review is to integrate information from a series of studies, employing a paradigm that evaluates local contextual processing using electrophysiological measures. Collectively these studies provide an overview of how utilization of predictive context changes as a function of stimulus modality and across different patient populations, as well as the networks that may be critical for this function. The following aspects of local contextual processing will be discussed and reviewed: (i) the correlates associated with contextual processing that have been identified in healthy adults, (ii) stimulus modality effects, (iii) specific alterations and deficits of local contextual processing in aging and across different neurological and psychiatric patient populations, including patients with prefrontal cortex lesions, Parkinson's disease, schizophrenia, and major depressive disorder, (iv) the potential for utilizing the correlates of local context as biomarkers for frontal cognitive dysfunction and (v) the role of frontal networks in the processing of contextual information. Overall findings show that behavioral and neural correlates associated with processing of local context are comparable across stimulus modalities, but show specific alterations in aging and across different neurological and psychiatric disorders.

The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding

This paper tests the hypothesis that patients with schizophrenia have a deficit in selectively attending to predictable events. We used dynamic causal modeling (DCM) of electrophysiological responses - to predictable and unpredictable visual targets - to quantify the effective connectivity within and between cortical sources in the visual hierarchy in 25 schizophrenia patients and 25 age-matched controls. We found evidence for marked differences between normal subjects and schizophrenia patients in the strength of extrinsic backward connections from higher hierarchical levels to lower levels within the visual system. In addition, we show that not only do schizophrenia subjects have abnormal connectivity but also that they fail to adjust or optimize this connectivity when events can be predicted. Thus, the differential intrinsic recurrent connectivity observed during processing of predictable versus unpredictable targets was markedly attenuated in schizophrenia patients compared with controls, suggesting a failure to modulate the sensitivity of neurons responsible for passing sensory information of prediction errors up the visual cortical hierarchy. The findings support the proposed role of abnormal connectivity in the neuropathology and pathophysiology of schizophrenia.