Mutual-information-based approach for neural connectivity during self-paced finger lifting task

Non-Equilibrium Enhancement of Classical Information Transmission

Information transmission plays a crucial role across various fields, including physics, engineering, biology, and society. The efficiency of this transmission is quantified by mutual information and its associated information capacity. While studies in closed systems have yielded significant progress, understanding the impact of non-equilibrium effects on open systems remains a challenge. These effects, characterized by the exchange of energy, information, and materials with the external environment, can influence both mutual information and information capacity. Here, we delve into this challenge by exploring non-equilibrium effects using the memoryless channel model, a cornerstone of information channel coding theories and methodology development. Our findings reveal that mutual information exhibits a convex relationship with non-equilibriumness, quantified by the non-equilibrium strength in transmission probabilities. Notably, channel information capacity is enhanced by non-equilibrium effects. Furthermore, we demonstrate that non-equilibrium thermodynamic cost, characterized by the entropy production rate, can actually improve both mutual information and information channel capacity, leading to a boost in overall information transmission efficiency. Our numerical results support our conclusions.

Enhanced Descending Corticomuscular Coupling During Hand Grip With Static Force Compared With Enhancing Force

The interaction between cortex and muscles under hand motor with different force states has not been quantitatively investigated yet, which to some extent places the optimized movement tasks design for brain-computer interface (BCI) applications in hand motor rehabilitation under uncertainty. Converging evidence has suggested that both the descending corticospinal pathway and ascending sensory feedback pathway are involved in the generation of corticomuscular coupling. The present study aimed to explore the corticomuscular coupling during hand motor task with enhancing force and steady-state force. Twenty healthy subjects performed precision grip with enhancing and static force using the right hand with visual feedback of exerted force. Mutual information and Granger causal connectivity were assessed between electroencephalography (EEG) over primary motor cortex and electromyography (EMG) recordings, and statistically analyzed. The results showed that the mutual information value was significantly larger for static force in the beta and alpha frequency band than enhancing force state. Furthermore, compared with enhancing force, the Granger causal connectivity of descending pathways from cortex to muscle was significantly larger for static force in the beta and high alpha frequency band (10-20 Hz), indicating the connection between the primary motor cortex and muscle was strengthened for static force. In summary, the hand grip with static force resulted in an increasing corticomuscular coupling from EEG over the primary motor cortex to EMG compared with enhancing force, implying more attention was required in the static force state. These results have important implications toward motor rehabilitation therapy design for the recovery of impaired hand motor functions.

Disrupted cortico-peripheral interactions in motor disorders

Motor disorders may arise from neurological damage or diseases at different levels of the hierarchical motor control system and side-loops. Altered cortico-peripheral interactions might be essential characteristics indicating motor dysfunctions. By integrating cortical and peripheral responses, top-down and bottom-up cortico-peripheral coupling measures could provide new insights into the motor control and recovery process. This review first discusses the neural bases of cortico-peripheral interactions, and corticomuscular coupling and corticokinematic coupling measures are addressed. Subsequently, methodological efforts are summarized to enhance the modeling reliability of neural coupling measures, both linear and nonlinear approaches are introduced. The latest progress, limitations, and future directions are discussed. Finally, we emphasize clinical applications of cortico-peripheral interactions in different motor disorders, including stroke, neurodegenerative diseases, tremor, and other motor-related disorders. The modified interaction patterns and potential changes following rehabilitation interventions are illustrated. Altered coupling strength, modified coupling directionality, and reorganized cortico-peripheral activation patterns are pivotal attributes after motor dysfunction. More robust coupling estimation methodologies and combination with other neurophysiological modalities might more efficiently shed light on motor control and recovery mechanisms. Future studies with large sample sizes might be necessary to determine the reliabilities of cortico-peripheral interaction measures in clinical practice.

Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

Background

The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients.

Methods

We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients.

Results

The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group.

Conclusions

The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

Time-Frequency Maximal Information Coefficient Method and its Application to Functional Corticomuscular Coupling

An important challenge in the study of functional corticomuscular coupling (FCMC) is an accurate capture of the coupling relationship between the cerebral cortex and the effector muscle. The coherence method is a linear analysis method, which has certain limitations in further revealing the nonlinear coupling between neural signals. Although mutual information (MI) and transfer entropy (TE) based on information theory can capture both linear and nonlinear correlations, the equitability of these algorithms is ignored and the nonlinear components of the correlation cannot be separated. The maximal information coefficient (MIC) is a suitable method to measure the coupling between neurophysiological signals. This study extends the MIC to the time-frequency domain, named time-frequency maximal information coefficient (TFMIC), to explore the FCMC in a specific frequency band. The effectiveness, equitability, and robustness of the algorithm on the simulation data was verified and compared with coherence, TE- and MI- based methods. Simulation results showed that the TFMIC could accurately detect the coupling for different functional relationships at low noise levels. The dorsiflexion experimental results revealed that the beta-band (14-30 Hz) significant coupling was observed at channels Cz, C4, FC4, and FCz. Additionally, the results showed that the coupling was higher in the alpha-band (8-13 Hz) and beta-band (14-30 Hz) than in the gamma-band (31-45 Hz). This might be related to a transition between sensorimotor states. Specifically, the nonlinear component of FCMC was also observed at channels Cz, C4, FC4, and FCz. This study expanded the research on nonlinear coupling components in FCMC.

Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analysis of neural codes by decomposing the EMG, also known as neural decoding, is an alternative to EMG amplitude estimation. In this study, we propose a fully-deterministic hybrid surface EMG (sEMG) decomposition approach that combines the advantages of both template-based and Blind Source Separation (BSS) decomposition approaches, a.k.a. guided source separation (GSS), to identify motor unit (MU) firing patterns. We use the single-pass density-based clustering algorithm to identify possible cluster representatives in different sEMG channels. These cluster representatives are then used as initial points of modified gradient Convolution Kernel Compensation (gCKC) algorithm. Afterwards, we use the Kalman filter to reduce the noise impact and increase convergence rate of MU filter identification by gCKC. Moreover, we designed an adaptive soft-thresholding method to identify MU firing times out of estimated MU spike trains. We tested the proposed algorithm on a set of synthetic sEMG signals with known MU firing patterns. A grid of 9 × 10 monopolar surface electrodes with 5-mm inter-electrode distances in both directions was simulated. Muscle excitation was set to 10, 30, and 50%. Colored Gaussian zero-mean noise with the signal-to-noise ratio (SNR) of 10, 20, and 30 dB, respectively, was added to 16 s long sEMG signals that were sampled at 4,096 Hz. Overall, 45 simulated signals were analyzed. Our decomposition approach was compared with gCKC algorithm. Overall, in our algorithm, the average numbers of identified MUs and Rate-of-Agreement (RoA) were 16.41 ± 4.18 MUs and 84.00 ± 0.06%, respectively, whereas the gCKC identified 12.10 ± 2.32 MUs with the average RoA of 90.78 ± 0.08%. Therefore, the proposed GSS method identified more MUs than the gCKC, with comparable performance. Its performance was dependent on the signal quality but not the signal complexity at different force levels. The proposed algorithm is a promising new offline tool in clinical neurophysiology.

Novel gait training alters functional brain connectivity during walking in chronic stroke patients: a randomized controlled pilot trial

Background

A recent study has demonstrated that a turning-based treadmill program yields greater improvements in gait speed and temporal symmetry than regular treadmill training in chronic stroke patients. However, it remains unknown how this novel and challenging gait training shapes the cortico-cortical network and cortico-spinal network during walking in chronic stroke patients. The purpose of this study was to examine how a novel type of gait training, which is an unfamiliar but effective task for people with chronic stroke, enhances brain reorganization.

Methods

Subjects in the experimental and control groups received 30 min of turning-based treadmill training and regular treadmill training, respectively. Cortico-cortical connectivity and cortico-muscular connectivity during walking and gait performance were assessed before and after completing the 12-session training.

Results

Eighteen subjects (n = 9 per group) with a mean age of 52.5 ± 9.7 years and an overground walking speed of 0.61 ± 0.26 m/s consented and participated in this study. There were significant group by time interactions for gait speed, temporal gait symmetry, and cortico-cortical connectivity as well as cortico-muscular connectivity in walk-related frequency (24–40 Hz) over the frontal-central-parietal areas. Compared with the regular treadmill training, the turning-based treadmill training resulted in greater improvements in these measures. Moreover, the increases in cortico-cortical connectivity and cortico-muscular connectivity while walking were associated with improvements in temporal gait symmetry.

Conclusions

Our findings suggest this novel turning-based treadmill training is effective for enhancing brain functional reorganization underlying cortico-cortical and corticomuscular mechanisms and thus may result in gait improvement in people with chronic stroke.

Trial registration

ACTRN12617000190303. Registered 3 February 2017, retrospectively registered.

Effect of pulsed transcranial ultrasound stimulation at different number of tone-burst on cortico-muscular coupling

Background

Pulsed transcranial ultrasound stimulation (pTUS) can modulate the neuronal activity of motor cortex and elicit muscle contractions. Cortico-muscular coupling (CMC) can serve as a tool to identify interaction between the oscillatory activity of the motor cortex and effector muscle. This research aims to explore the neuromodulatory effect of low-intensity, pTUS with different number of tone burst to neural circuit of motor-control system by analyzing the coupling relationship between motor cortex and tail muscle in mouse. The motor cortex of mice was stimulated by pulsed transcranial ultrasound with different number of tone bursts (NTB = 100 150 200 250 300). The local field potentials (LFPs) in tail motor cortex and electromyography (EMG) in tail muscles were recorded simultaneously during pTUS. The change of integral coupling strength between cortex and muscle was evaluated by mutual information (MI). The directional information interaction between them were analyzed by transfer entropy (TE).

Results

Almost all of the MI and TE values were significantly increased by pTUS. The results of MI showed that the CMC was significantly enhanced with the increase of NTB. The TE results showed the coupling strength of CMC in descending direction (from LFPs to EMG) was significantly higher than that in ascending direction (from EMG to LFPs) after stimulation. Furthermore, compared to NTB = 100, the CMC in ascending direction were significantly enhanced when NTB = 250, 300, and CMC in descending direction were significantly enhanced when NTB = 200, 250, 300.

Conclusion

These results confirm that the CMC between motor cortex and the tail muscles in mouse could be altered by pTUS. And by increasing the NTB (i.e. sonication duration), the coupling strength within the cortico-muscular circuit could be increased, which might further influence the motor function of mice. It demonstrates that, using MI and TE method, the CMC could be used for quantitatively evaluating the effect of pTUS with different NTBs, which might provide a new insight into the effect of pTUS neuromodulation in motor cortex.

Electrophysiological resting-state biomarker for diagnosing mesial temporal lobe epilepsy with hippocampal sclerosis

The main aim of the present study was to evaluate whether resting-state functional connectivity of magnetoencephalography (MEG) signals can differentiate patients with mesial temporal lobe epilepsy (MTLE) from healthy controls (HC) and can differentiate between right and left MTLE as a diagnostic biomarker. To this end, a support vector machine (SVM) method among various machine learning algorithms was employed. We compared resting-state functional networks between 46 MTLE (right MTLE=23; left MTLE=23) patients with histologically proven HS who were free of seizure after surgery, and 46 HC. The optimal SVM group classifier distinguished MTLE patients with a mean accuracy of 95.1% (sensitivity=95.8%; specificity=94.3%). Increased connectivity including the right posterior cingulate gyrus and decreased connectivity including at least one sensory-related resting-state network were key features reflecting the differences between MTLE patients and HC. The optimal SVM model distinguished between right and left MTLE patients with a mean accuracy of 76.2% (sensitivity=76.0%; specificity=76.5%). We showed the potential of electrophysiological resting-state functional connectivity, which reflects brain network reorganization in MTLE patients, as a possible diagnostic biomarker to differentiate MTLE patients from HC and differentiate between right and left MTLE patients.

MIC as an appropriate method to construct the brain functional network

Using an effective method to measure the brain functional connectivity is an important step to study the brain functional network. The main methods for constructing an undirected brain functional network include correlation coefficient (CF), partial correlation coefficient (PCF), mutual information (MI), wavelet correlation coefficient (WCF), and coherence (CH). In this paper we demonstrate that the maximal information coefficient (MIC) proposed by Reshef et al. is relevant to constructing a brain functional network because it performs best in the comprehensive comparisons in consistency and robustness. Our work can be used to validate the possible new functional connection measures.

Abnormal functional brain network in epilepsy patients with focal cortical dysplasia

PURPOSE

Focal cortical dysplasia (FCD) is the second most common pathological entity in surgically treated neocortical focal epilepsy. Despite the recent increase of interest in network approaches derived from graph theory on epilepsy, resting state network analysis of the FCD brain has not been adequately investigated. In this study, we investigated the difference in the resting state functional network between epilepsy patients with FCD and healthy controls using whole-brain magnetoencephalography (MEG) recordings.

METHODS

Global mutual information (MIglob) and global efficiency (Eglob) were calculated for theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (31-45 Hz) bands in 35 epilepsy patients with FCD and 23 healthy controls.

RESULTS

Resting state FCD brains had stronger functional connectivity (MIglob) in the beta and gamma bands and higher functional efficiency (Eglob) in the beta and gamma bands than those of the controls (p<0.05). The MIglob and Eglob values of FCD type I and II brains in the beta band were higher than those of healthy control brains (p<0.05). In the gamma band, the values of FCD type II brains were higher than those of control and FCD type I brains (p<0.05).

CONCLUSIONS

FCD brains had increased functional connectivity in the beta and gamma frequency bands at the resting state compared with those in healthy controls. In addition, patients exhibited different network characteristics depending on the type of FCD. The resting state network analysis could be useful in a clinical setting because we observed network differences even when there was no prominent interictal spike activity.

A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment

Recently, the synchronization between neural signals has been widely used as a key indicator of brain function. To understand comprehensively the effect of synchronization on the brain function, accurate computation of the synchronization strength among multivariate neural series from the whole brain is necessary. In this study, we proposed a method named global coupling index (GCI) to estimate the synchronization strength of multiple neural signals. First of all, performance of the GCI method was evaluated by analyzing simulated EEG signals from a multi-channel neural mass model, including the effects of the frequency band, the coupling coefficient, and the signal noise ratio. Then, the GCI method was applied to analyze the EEG signals from 12 mild cognitive impairment (MCI) subjects and 12 normal controls (NC). The results showed that GCI method had two major advantages over the global synchronization index (GSI) or S-estimator. Firstly, simulation data showed that the GCI method provided both a more robust result on the frequency band and a better performance on the coupling coefficients. Secondly, the actual EEG data demonstrated that GCI method was more sensitive in differentiating the MCI from control subjects, in terms of the global synchronization strength of neural series of specific alpha, beta1 and beta2 frequency bands. Hence, it is suggested that GCI is a better method over GSI and S-estimator to estimate the synchronization strength of multivariate neural series for predicting the MCI from the whole brain EEG recordings.

Preserved high-centrality hubs but efficient network reorganization during eyes-open state compared with eyes-closed resting state: an MEG study

A question to be addressed in the present study is how different the eyes-closed (EC) and eyes-open (EO) resting states are across frequency bands in terms of efficiency and centrality of the brain functional network. We investigated both the global and nodal efficiency and betweenness centrality in the EC and EO resting states from 39 volunteers. Mutual information was used to obtain the functional connectivity for each of the four frequency bands (theta, alpha, beta, and gamma). We showed that the cortical hubs with high betweenness centrality were maintained in the EC and EO resting states. We further showed that these hubs were associated with more than three frequency bands, suggesting that these hubs play an important role in the brain functional network at multiple temporal scales in the resting states. Enhanced global efficiency values were found in the theta and alpha bands in the EO state compared with those in the EC state. Moreover, it turned out that in the EO state the functional network was reorganized to enhance nodal efficiency at the nodes related to both the default mode and the dorsal attention networks and sensory-related resting-state networks. This result suggests that in the EO state the brain functional network was efficiently reorganized, facilitating the adaptation of the brain network to the change in state, which could help in understanding brain disorders that have a disturbance in communication with external environments by using the adaptation ability of brain functional networks.

Understanding alcohol use disorders with neuroelectrophysiology

Neurocognitive deficits associated with impairments in various brain regions and neural circuitries, particularly involving frontal lobes, have been associated with chronic alcoholism, as well as with a predisposition to develop alcohol use and related disorders (AUDs). AUD is a multifactorial disorder caused by complex interactions between behavioral, genetic, and environmental liabilities. Neuroelectrophysiologic techniques are instrumental in understanding brain and behavior relationships and have also proved very useful in evaluating the genetic diathesis of alcoholism. This chapter describes findings from neuroelectrophysiologic measures (electroencephalogram, event-related potentials, and event-related oscillations) related to acute and chronic effects of alcohol on the brain and those that reflect underlying deficits related to a predisposition to develop AUDs and related disorders. The utility of these measures as effective endophenotypes to identify and understand genes associated with brain electrophysiology, cognitive networks, and AUDs has also been discussed.

Neural synchrony within the motor system: what have we learned so far?

Synchronization of neural activity is considered essential for information processing in the nervous system. Both local and inter-regional synchronization are omnipresent in different frequency regimes and relate to a variety of behavioral and cognitive functions. Over the years, many studies have sought to elucidate the question how alpha/mu, beta, and gamma synchronization contribute to motor control. Here, we review these studies with the purpose to delineate what they have added to our understanding of the neural control of movement. We highlight important findings regarding oscillations in primary motor cortex, synchronization between cortex and spinal cord, synchronization between cortical regions, as well as abnormal synchronization patterns in a selection of motor dysfunctions. The interpretation of synchronization patterns benefits from combining results of invasive and non-invasive recordings, different data analysis tools, and modeling work. Importantly, although synchronization is deemed to play a vital role, it is not the only mechanism for neural communication. Spike timing and rate coding act together during motor control and should therefore both be accounted for when interpreting movement-related activity.

Clock drawing in spatial neglect: a comprehensive analysis of clock perimeter, placement, and accuracy

Clock drawings produced by right-brain-damaged (RBD) individuals with spatial neglect often contain an abundance of empty space on the left while numbers and hands are placed on the right. However, the clock perimeter is rarely compromised in neglect patients' drawings. By analysing clock drawings produced by 71 RBD and 40 healthy adults, this study investigated whether the geometric characteristics of the clock perimeter reveal novel insights to understanding spatial neglect. Neglect participants drew smaller clocks than either healthy or non-neglect RBD participants. While healthy participants' clock perimeter was close to circular, RBD participants drew radially extended ellipses. The mechanisms for these phenomena were investigated by examining the relation between clock-drawing characteristics and performance on six subtests of the Behavioral Inattention Test (BIT). The findings indicated that the clock shape was independent of any BIT subtest or the drawing placement on the test sheet and that the clock size was significantly predicted by one BIT subtest: the poorer the figure and shape copying, the smaller the clock perimeter. Further analyses revealed that in all participants, clocks decreased in size as they were placed farther from the centre of the paper. However, even when neglect participants placed their clocks towards the centre of the page, they were smaller than those produced by healthy or non-neglect RBD participants. These results suggest a neglect-specific reduction in the subjectively available workspace for graphic production from memory, consistent with the hypothesis that neglect patients are impaired in the ability to enlarge the attentional aperture.

The influence of dopamine receptor d4 polymorphism on resting EEG in healthy young females

The polymorphism of variable number of tandem repeat (VNTR) in dopamine receptor D4 (DRD4) gene exon III has been linked to various neuro-psychiatric conditions with disinhibition/impulsivity as one of the core features. This study examined the modulatory effects of long-allele variant of DRD4 VNTR on the regional neural activity as well as inter-regional neural interactions in a young female population. Blood sample and resting state eyes-closed EEG signals were collected in 233 healthy females, stratified into two groups by polymerase chain reaction: long-allele carriers (>4- repeat) and non-carriers (<=4-repeat/<=4-repeat). The values of mean power of 18 electrodes and mutual information of 38 channel pairs across theta, alpha, and beta frequencies were analyzed. Our connectivity analysis was based on information theory, which combined Morlet wavelet transform and mutual information calculation. Between-group differences of regional power and connectivity strength were quantified by independent t-test, while between-group differences in global trends were examined by non-parametric analyses. We noticed that DRD4 VNTR long-allele was associated with decreased global connectivity strength (from non-parametric analysis), especially over bi-frontal, biparietal and right fronto-parietal and right fronto-temporal connections (from independent t-tests). The between-group differences in regional power were not robust. Our findings fit with the networks of response inhibition, providing evidence bridging DRD4 long-allele and disinhibition/impulsivity in neuropsychiatric disorders. We suggest future DRD4 studies of imaging genetics incorporate connectivity analysis to unveil its impact on cerebral network.

Event-Related Oscillations in Alcoholism Research: A Review

Alcohol dependence is characterized as a multi-factorial disorder caused by a complex interaction between genetic and environmental liabilities across development. A variety of neurocognitive deficits/dysfunctions involving impairments in different brain regions and/or neural circuitries have been associated with chronic alcoholism, as well as with a predisposition to develop alcoholism. Several neurobiological and neurobehavioral approaches and methods of analyses have been used to understand the nature of these neurocognitive impairments/deficits in alcoholism. In the present review, we have examined relatively novel methods of analyses of the brain signals that are collectively referred to as event-related oscillations (EROs) and show promise to further our understanding of human brain dynamics while performing various tasks. These new measures of dynamic brain processes have exquisite temporal resolution and allow the study of neural networks underlying responses to sensory and cognitive events, thus providing a closer link to the physiology underlying them. Here, we have reviewed EROs in the study of alcoholism, their usefulness in understanding dynamical brain functions/dysfunctions associated with alcoholism as well as their utility as effective endophenotypes to identify and understand genes associated with both brain oscillations and alcoholism.

Abnormal functional connectivity in focal hand dystonia: mutual information analysis in EEG

The aim of the present study was to investigate functional connectivity in focal hand dystonia patients to understand the pathophysiology underlying their abnormality in movement. We recorded EEGs from 58 electrodes in 15 focal hand dystonia patients and 15 healthy volunteers during rest and a simple finger-tapping task that did not induce any dystonic symptoms. We investigated mutual information, which provides a quantitative measure of linear and nonlinear coupling, in the alpha, beta, and gamma bands. Mean mutual information of all 58 channels and mean of the channels of interest representative of regional functional connectivity over sensorimotor areas (C3, CP3, C4, CP4, FCz, and Cz) were evaluated. For both groups, we found enhanced mutual information during the task compared with the rest condition, specifically in the beta and gamma bands for mean mutual information of all channels, and in all bands for mean mutual information of channels of interest. Comparing the focal hand dystonia patients with the healthy volunteers for both rest and task, there was reduced mutual information in the beta band for both mean mutual information of all channels and mean mutual information of channels of interest. Regarding the properties of the connectivity in the beta band, we found that the majority of the mutual information differences were from linear connectivity. The abnormal beta-band functional connectivity in focal hand dystonia patients suggests deficient brain connectivity.

Rapid eye movement sleep atonia in patients with cognitive impairment

Acetylcholine (ACh) plays an important role in cognitive function and muscle atonia in rapid eye movement (REM) sleep. This quantitative study used surface electromyography (sEMG) to investigate changes in muscular activity, which may indicate a deficiency of ACh among patients with cognitive impairment. We recruited 9 controls without dementia, 6 patients with mild cognitive impairment (MCI), and 6 patients with mild Alzheimer's disease (AD). None of the participants had sleep complaints, and all AD patients were receiving cholinesterase inhibitors. Subjects underwent polysomnography (PSG), including sEMG of the leg muscles, which was analyzed using root mean square (rms), mean frequency, and peak frequency. The average rms values during REM sleep among the control, MCI, and AD patients were 0.553 ± 0.177, 2.886 ± 2.865, and 0.909 ± 0.822, respectively. The average peak frequencies during REM sleep in control, MCI, and AD patients were 0.402 ± 0.758, 30.524 ± 32.237, and 0.566 ± 1.153, respectively. The average mean frequencies during REM sleep in control, MCI, and AD patients were 8.849 ± 6.071, 34.530 ± 25.564, and 9.553 ± 6.308, respectively. All rms, mean frequencies, and peak frequencies increased significantly (p<0.05) in MCI patients. A deficiency of ACh may result in an increase of sEMG activity in MCI patients. Because cholinesterase inhibitors are capable of suppressing sEMG activity in AD patients, we speculate that an increase in sEMG activity is associated with a deficiency of Ach, which could be an early indicator of dementia.

Resting functional connectivity in patients with brain tumors in eloquent areas

OBJECTIVE

Resection of brain tumors adjacent to eloquent areas represents a challenge in neurosurgery. If maximal resection is desired without inducing postoperative neurological deficits, a detailed knowledge of the functional topography in and around the tumor is crucial. The aim of the present work is to evaluate the value of preoperative magnetoencephalography (MEG) imaging of functional connectivity to predict the results of intraoperative electrical stimulation (IES) mapping, the clinical gold standard for neurosurgical localization of functional areas.

METHODS

Resting-state whole-cortex MEG recordings were obtained from 57 consecutive subjects with focal brain tumors near or within motor, sensory, or language areas. Neural activity was estimated using adaptive spatial filtering algorithms, and the mean imaginary coherence between the rest of the brain and voxels in and around brain tumors were compared to the mean imaginary coherence between the rest of the brain and contralesional voxels as an index of functional connectivity. IES mapping was performed in all subjects. The cortical connectivity pattern near the tumor was compared to the IES results.

RESULTS

Maps with decreased resting-state functional connectivity in the entire tumor area had a negative predictive value of 100% for absence of eloquent cortex during IES. Maps showing increased resting-state functional connectivity within the tumor area had a positive predictive value of 64% for finding language, motor, or sensory cortical sites during IES mapping.

INTERPRETATION

Preoperative resting state MEG connectivity analysis is a useful noninvasive tool to evaluate the functionality of the tissue surrounding tumors within eloquent areas, and could potentially contribute to surgical planning and patient counseling.

Reorganization of functional connectivity during the motor task using EEG time-frequency cross mutual information analysis

OBJECTIVE

This study investigates the functional organization of cortical networks during self-determinant arm movement using the time sequences of the alpha (8-12 Hz) and beta (16-25 Hz) bands.

METHODS

The time-frequency cross mutual information (TFCMI) method was used to estimate the EEG functional connectivity in the alpha and beta bands for seven healthy subjects during four functional states: the resting, preparing, movement-onset, and movement-offset states.

RESULTS

In the preparing state, the maintenance of the central-executive network (CEN, prefrontal-parietal connection) suppressed the motor network in the alpha band to plan the next movement, whereas the CEN was deactivated in the beta band to retain visual attention (the frontal-occipital connection). A significant decrease of the CEN in the alpha band occurred after a visual cue in the movement-onset state, followed by a significant increase in motor-network connectivity in the beta band until the movement-offset state.

CONCLUSIONS

The temporal-spectral modulation mechanism allows the brain to manifest multiple functions subject to energy budget.

SIGNIFICANCE

The TFCMI method was employed to estimate EEG functional connectivity and effectively demonstrate the reorganization process between four functional states.

The effects of catechol-O-methyl-transferase polymorphism Val158Met on functional connectivity in healthy young females: a resting EEG study

The catechol-O-methyl-transferase (COMT) gene has been linked to a wide spectrum of human phenotypes, including cognition, affective response, pain sensitivity, anxiety and psychosis. This study examined the modulatory effects of COMT Val158Met on neural interactions, indicated by connectivity strengths. Blood samples and resting state eyes-closed EEG signals were collected in 254 healthy young females. The COMT Val158Met polymorphism was decoded into 3 groups: Val/Val, Val/Met and Met/Met. The values of mutual information of 20 frontal-related channel pairs across delta, theta, alpha and beta frequencies were analyzed based on the time-frequency mutual information method. Our one-way ANOVA analyses revealed that the significant connection-frequency pairs were relatively left lateralized (P<0.01) and included F7-T3 and F7-C3 at delta frequency, and F3-F4, F7-T3, F7-C3, F7-P3, F3-C3, F3-F7 and F4-F8 at theta frequency. The F-test at F7-T3 and F7-C3 theta surpassed the statistical threshold of P<0.003 (after Bonferroni correction). For all the above connection-frequency pairs, there was a dose-dependent trend in the connectivity strengths of the alleles as follows: Val/Val>Val/Met>Met/Met. Our analyses complemented previous literature regarding neural modulation by the COMT Val158Met polymorphism. The implication to the pathogenesis in schizophrenia was also discussed. Further studies are needed to clarify whether there is gender difference on this gene-brain interaction.

Modulus and direction of the neural current vector identify distinct functional connectivity modes between human MT+ areas

Reconstruction of neural current sources from magnetoencephalography (MEG) data provides two independent estimates of the instantaneous current modulus and its direction. Here, we explore how different information on the modulus and direction affects the inter-hemisphere connectivity of the human medial temporal complex (hMT+). Connectivity was quantified by mutual information values of paired time series of current moduli or directions, with the joint probability distribution estimated with an optimized Gaussian kernel. These time series were obtained from tomographic analysis of single-trial MEG responses to a visual motion stimulus. With a high-contrast stimulus, connectivity measures based on the modulus were relatively strong in the prestimulus period, continuing until 100 ms after stimulus onset. The strongest modulus connectivity was produced with a long lag (19 ms) of the right hMT+ after the left hMT+. On the other hand, connectivity measures based on direction were relatively strong after 100 ms, with a short delay of less than 6 ms. These results suggest that nonspecific and probably indirect communication between the homologous areas is turned, by the stimulus arrival, into more precise and direct communication through the corpus callosum. The orientation of the estimated current vector for the strong connectivity can be explained by the curvature of the active cortical sheet. The temporal patterns of modulus and directional connectivity were different at low contrast, but similar to those at high contrast. We conclude that the modulus and direction indicate distinct functional connectivity modes.

Drug effect on EEG connectivity assessed by linear and nonlinear couplings

Quantitative analysis of human electroencephalogram (EEG) is a valuable method for evaluating psychopharmacological agents. Although the effects of different drug classes on EEG spectra are already known, interactions between brain locations remain unclear. In this work, cross mutual information function and appropriate surrogate data were applied to assess linear and nonlinear couplings between EEG signals. The main goal was to evaluate the pharmacological effects of alprazolam on brain connectivity during wakefulness in healthy volunteers using a cross-over, placebo-controlled design. Eighty-five pairs of EEG leads were selected for the analysis, and connectivity was evaluated inside anterior, central, and posterior zones of the scalp. Connectivity between these zones and interhemispheric connectivity were also measured. Results showed that alprazolam induced significant changes in EEG connectivity in terms of information transfer in comparison with placebo. Trends were opposite depending on the statistical characteristics: decreases in linear connectivity and increases in nonlinear couplings. These effects were generally spread over the entire scalp. Linear changes were negatively correlated, and nonlinear changes were positively correlated with drug plasma concentrations; the latter showed higher correlation coefficients. The use of both linear and nonlinear approaches revealed the importance of assessing changes in EEG connectivity as this can provide interesting information about psychopharmacological effects.

Functional brain imaging in schizophrenia: selected results and methods

Functional brain imaging studies of patients with schizophrenia may be grouped into those that assume that the signs and symptoms of schizophrenia are due to disordered circuitry within a critical brain region and studies that assume that the signs and symptoms are due to disordered connections among brain regions. Studies have investigated the disordered functional brain anatomy of both the positive and negative symptoms of schizophrenia. Studies of spontaneous hallucinations find that although hallucinations are associated with abnormal brain activity in primary and secondary sensory areas, disordered brain activation associated with hallucinations is not limited to sensory systems. Disordered activation in non-sensory regions appear to contribute to the emotional strength and valence of hallucinations, to be a factor underlying an inability to distinguish ongoing mental processing from memories, and to reflect the brain's attempt to modulate the intensity of hallucinations and resolve conflicts with other processing demands. Brain activation studies support the view that auditory/verbal hallucinations are associated with an impaired ability of internal speech plans to modulate neural activation in sensory language areas. In early studies, negative symptoms of schizophrenia were hypothesized to be associated with impaired function in frontal brain areas. In support of this hypothesis meta-analytical studies have found that resting blood flow or metabolism in frontal cortex is reduced in schizophrenia, though the magnitude of the effect is only small to moderate. Brain activation studies of working memory (WM) functioning are typically associated with large effect sizes in the frontal cortex, whereas studies of functions other than WM generally reveal smaller effects. Findings from some functional connectivity studies have supported the hypothesis that schizophrenia patients experience impaired functional connections between frontal and temporal cortex, although the nature of the disordered connectivity is complex. More recent studies have used functional brain imaging to study neural compensation in schizophrenia, to serve as endophenotypes in genetic studies and to provide biomarkers in drug development studies. These emerging trends in functional brain imaging research are likely to help stimulate the development of a general neurobiological theory of the complex symptoms of schizophrenia.

Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction

OBJECTIVE

To propose a model-free method to show linear and nonlinear information flow based on time-delayed mutual information (TDMI) by employing uni- and bi-variate surrogate tests and to investigate whether there are contributions of the nonlinear information flow in corticomuscular (CM) interaction.

METHODS

Using simulated data, we tested whether our method would successfully detect the direction of information flow and identify a relationship between two simulated time series. As an experimental data application, we applied this method to investigate CM interaction during a right wrist extension task.

RESULTS

Results of simulation tests show that we can correctly detect the direction of information flow and the relationship between two time series without a prior knowledge of the dynamics of their generating systems. As experimental results, we found both linear and nonlinear information flow from contralateral sensorimotor cortex to muscle.

CONCLUSIONS

Our method is a viable model-free measure of temporally varying causal interactions that is capable of distinguishing linear and nonlinear information flow. With respect to experimental application, there are both linear and nonlinear information flows in CM interaction from contralateral sensorimotor cortex to muscle, which may reflect the motor command from brain to muscle.

SIGNIFICANCE

This is the first study to show separate linear and nonlinear information flow in CM interaction.

MATLAB toolbox for functional connectivity

The term "functional connectivity" is used to denote correlations in activation among spatially-distinct brain regions, either in a resting state or when processing external stimuli. Functional connectivity has been extensively evaluated with several functional neuroimaging methods, particularly PET and fMRI. Yet these relationships have been quantified using very different measures and the extent to which they index the same constructs is unclear. We have implemented a variety of these functional connectivity measures in a new freely available MATLAB toolbox. These measures are categorized into two groups: whole time-series and trial-based approaches. We evaluate these measures via simulations with different patterns of functional connectivity and provide recommendations for their use. We also apply these measures to a previously published fMRI data set (Siegle, G.J., Thompson, W., Carter, C.S., Steinhauer, S.R., Thase, M.E., 2007. Increased amygdala and decreased dorsolateral prefrontal BOLD responses in unipolar depression: related and independent features. Biol. Psychiatry 610 (2), 198-209) in which activity in dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC) was evaluated in 32 control subjects during a digit sorting task. Though all implemented measures demonstrate functional connectivity between dACC and DLPFC activity during event-related tasks, different participants appeared to display qualitatively different relationships.

Comparing neural correlates of visual target detection in serial visual presentations having different temporal correlations

Most visual stimuli we experience on a day-to-day basis are continuous sequences, with spatial structure highly correlated in time. During rapid serial visual presentation (RSVP), this correlation is absent. Here we study how subjects' target detection responses, both behavioral and electrophysiological, differ between continuous serial visual sequences (CSVP), flashed serial visual presentation (FSVP) and RSVP. Behavioral results show longer reaction times for CSVP compared to the FSVP and RSVP conditions, as well as a difference in miss rate between RSVP and the other two conditions. Using mutual information, we measure electrophysiological differences in the electroencephalography (EEG) for these three conditions. We find two peaks in the mutual information between EEG and stimulus class (target vs. distractor), with the second peak occurring 30-40 ms earlier for the FSVP and RSVP conditions. In addition, we find differences in the persistence of the peak mutual information between FSVP and RSVP conditions. We further investigate these differences using a mutual information based functional connectivity analysis and find significant fronto-parietal functional coupling for RSVP and FSVP but no significant coupling for the CSVP condition. We discuss these findings within the context of attentional engagement, evidence accumulation and short-term visual memory.