Imaginary Part Of Coherency Research Articles

Pelvic floor muscle electrical coupling in chronic pelvic pain: Insights into pathophysiology and botulinum toxin treatment effects

This study aimed to assess the electrical coupling between both pelvic floor muscle (PFM) sides (two-sided coupling) and within individual PFM sides (one-sided coupling) in chronic pelvic pain (CPP) before and after botulinum neurotoxin type A (BoNT/A) treatment. Surface electromyographic (sEMG) signals were recorded from the left and right PFM of 24 patients (P) with CPP before and after being treated with BoNT/A (Weeks 0,8,12,24). Recordings were also made in 24 healthy women (H). PFM two-sided and one-sided coupling was evaluated during contractions by the cross-correlation (CC) and the imaginary part of coherency (iCOH) of their sEMG signals. Significant differences between their values were assessed comparing P(0) vs. P(8,12,24) and H vs. P(0,8,12,24). This study showed that PFM two-sided coupling is similar across groups before treatment, while PFM one-sided coupling on the patients’ most painful side is deranged before and also after BoNT/A treatment: amplitude coupling is lower (<CC) and phase difference is greater (>iCOH) than healthy women’s. This could be justified by altered neuromotor control strategies developed as an adaptation to muscle pain, structural and electrical changes in PFM, and alterations in their innervation pattern, which may influence the onset, perpetuation, or recurrence of CPP after treatment.

Identifying neural correlates of balance impairment in traumatic brain injury using partial least squares correlation analysis

Objective.Balance impairment is one of the most debilitating consequences of traumatic brain injury (TBI). To study the neurophysiological underpinnings of balance impairment, the brain functional connectivity during perturbation tasks can provide new insights. To better characterize the association between the task-relevant functional connectivity and the degree of balance deficits in TBI, the analysis needs to be performed on the data stratified based on the balance impairment. However, such stratification is not straightforward, and it warrants a data-driven approach.Approach.We conducted a study to assess the balance control using a computerized posturography platform in 17 individuals with TBI and 15 age-matched healthy controls. We stratified the TBI participants into balance-impaired and non-impaired TBI usingk-means clustering of either center of pressure (COP) displacement during a balance perturbation task or Berg Balance Scale score as a functional outcome measure. We analyzed brain functional connectivity using the imaginary part of coherence across different cortical regions in various frequency bands. These connectivity features are then studied using the mean-centered partial least squares correlation analysis, which is a multivariate statistical framework with the advantage of handling more features than the number of samples, thus making it suitable for a small-sample study.Main results.Based on the nonparametric significance testing using permutation and bootstrap procedure, we noticed that the weakened theta-band connectivity strength in the following regions of interest significantly contributed to distinguishing balance impaired from non-impaired population, regardless of the type of stratification:left middle frontal gyrus, right paracentral lobule, precuneus, andbilateral middle occipital gyri. Significance.Identifying neural regions linked to balance impairment enhances our understanding of TBI-related balance dysfunction and could inform new treatment strategies. Future work will explore the impact of balance platform training on sensorimotor and visuomotor connectivity.

Artificial Intelligence Model‐based Alzheimer’s Disease Dementia Classification Using Electroencephalogram‐acquired Brain Functional Network

AbstractBackgroundAlzheimer’s disease dementia (ADD) is a predominant neurodegenerative disorder that results severe abnormalities in cognitive functions. However, most diagnoses rely on assessments made by individual clinical experts.Hence, we aim to establish a novel biomarker which can provide both accuracy and objectivity. Electroencephalogram (EEG) data were utilized for the training of artificial intelligence (AI) models, making use of its cost‐advantage and simple utility.Method19‐channel EEG data were recorded at Chung‐Ang University hospital in accordance with international 10‐20 system at resting state with eyes being closed. We first converted 19 channels into 68 regions of interest (ROIs) using standardized low resolution brain electromagnetic tomography (sLORETA). Henceforth, imaginary part of coherence (iCoh) between each ROI were calculated, reflecting functional connectivity between the ROIs. The iCoh values were then utilized in the calculation of the brain’s functional brain network.The subjects were divided into non‐ADD and ADD group. The resulting training dataset consisted of 118 non‐ADD and 110 ADD subjects, where the number of non‐ADD training data were adjusted in order to account for data imbalance. The test dataset included 476 non‐ADD and 27 ADD subjects.The classification model was trained using 68 ROI source powers and functional brain network features. Only statistically significant features were selected for feature reduction purposes. Through primary classification via light gradient boosting method (LGBM), top 30 features with high importance values were selected and were employed in the training of our final LGBM classification model.ResultThrough appropriate hyperparameter tuning, the LGBM classification model resulted in 87.67% accuracy, 88.89% sensitivity and 87.61% specificity. From the receiver operating characteristic (ROC) curve, 0.9506 area under ROC curve (AUC) was achieved.ConclusionThe LGBM model resulted in promising classification performance, solely through EEG features. In addition, functional brain network features had higher importance in comparison with source power features, indicating high potential of EEG brain network features in distinguishment of neurodegenerative diseases.

MEG Node Degree for Focus Localization: Comparison with Invasive EEG.

Epilepsy surgery is a viable therapy option for patients with pharmacoresistant focal epilepsies. A prerequisite for postoperative seizure freedom is the localization of the epileptogenic zone, e.g., using electro- and magnetoencephalography (EEG/MEG). Evidence shows that resting state MEG contains subtle alterations, which may add information to the workup of epilepsy surgery. Here, we investigate node degree (ND), a graph-theoretical parameter of functional connectivity, in relation to the seizure onset zone (SOZ) determined by invasive EEG (iEEG) in a consecutive series of 50 adult patients. Resting state data were subjected to whole brain, all-to-all connectivity analysis using the imaginary part of coherence. Graphs were described using parcellated ND. SOZ localization was investigated on a lobar and sublobar level. On a lobar level, all frequency bands except alpha showed significantly higher maximal ND (mND) values inside the SOZ compared to outside (ratios 1.11-1.20, alpha 1.02). Area-under-the-curve (AUC) was 0.67-0.78 for all expected alpha (0.44, ns). On a sublobar level, mND inside the SOZ was higher for all frequency bands (1.13-1.38, AUC 0.58-0.78) except gamma (1.02). MEG ND is significantly related to SOZ in delta, theta and beta bands. ND may provide new localization tools for presurgical evaluation of epilepsy surgery.

Is Functional Connectivity after a First Unprovoked Seizure Different Based on Subsequent Seizures and Future Diagnosis of Epilepsy?

There are no highly sensitive biomarkers for epilepsy to date. Recently, promising results regarding functional connectivity analysis have been obtained, which may improve epilepsy diagnosis even in the absence of visible abnormality in electroencephalography. We aimed to investigate the differences in functional connectivity after a first unprovoked seizure between patients diagnosed with epilepsy within 1 year due to subsequent seizures and those who were not. We compared quantitative electroencephalography power spectra and functional connectivity between 12 patients who were diagnosed with epilepsy (two or more unprovoked seizures) within 1 year and 17 controls (those not diagnosed within 1 year) using iSyncBrain® (iMediSync Inc., Suwon, Korea; https://isyncbrain.com/). In the source-level analysis, the current distribution across the brain was assessed using the standardized low-resolution brain electromagnetic tomography technique, to compare relative power values in 68 regions of interest and connectivity (the imaginary part of coherency) between regions of interest. In the epilepsy group, quantitative electroencephalography showed lower alpha2 band power in left frontal, central, superior temporal, and parietal regions and higher beta2 power in both frontal, central, temporal, occipital, and left parietal regions compared with the control group. Additionally, epilepsy patients had significantly lower connectivity in alpha2 and beta2 bands than the controls. Patients experiencing their first unprovoked seizure presented different brain function according to whether they have subsequent seizures and future epilepsy. Our results propose the potential clinical ability to diagnose epilepsy after the first unprovoked seizure in the absence of interictal epileptiform discharges.

Is Functional Connectivity after a First Unprovoked Seizure Different Based on Subsequent Seizures and Future Diagnosis of Epilepsy?

Background and Purpose: There are no highly sensitive biomarkers for epilepsy to date. Recently, promising results regarding functional connectivity analysis have been obtained, which may improve epilepsy diagnosis even in the absence of visible abnormality in electroencephalography. We aimed to investigate the differences in functional connectivity after a first unprovoked seizure between patients diagnosed with epilepsy within 1 year due to subsequent seizures and those who were not.Methods: We compared quantitative electroencephalography power spectra and functional connectivity between 12 patients who were diagnosed with epilepsy (two or more unprovoked seizures) within 1 year and 17 controls (those not diagnosed within 1 year) using iSyncBrain&lt;sup&gt;®&lt;/sup&gt; (iMediSync Inc., Suwon, Korea; https://isyncbrain.com/). In the source-level analysis, the current distribution across the brain was assessed using the standardized low-resolution brain electromagnetic tomography technique, to compare relative power values in 68 regions of interest and connectivity (the imaginary part of coherency) between regions of interest.Results: In the epilepsy group, quantitative electroencephalography showed lower alpha2 band power in left frontal, central, superior temporal, and parietal regions and higher beta2 power in both frontal, central, temporal, occipital, and left parietal regions compared with the control group. Additionally, epilepsy patients had significantly lower connectivity in alpha2 and beta2 bands than the controls.Conclusions: Patients experiencing their first unprovoked seizure presented different brain function according to whether they have subsequent seizures and future epilepsy. Our results propose the potential clinical ability to diagnose epilepsy after the first unprovoked seizure in the absence of interictal epileptiform discharges.

Neuronal networks underlying ictal and subclinical discharges in childhood absence epilepsy

Childhood absence epilepsy (CAE), involves 3 Hz generalized spikes and waves discharges (GSWDs) on the electroencephalogram (EEG), associated with ictal discharges (seizures) with clinical symptoms and impairment of consciousness and subclinical discharges without any objective clinical symptoms or impairment of consciousness. This study aims to comparatively characterize neuronal networks underlying absence seizures and subclinical discharges, using source localization and functional connectivity (FC), to better understand the pathophysiological mechanism of these discharges. Routine EEG data from 12 CAE patients, consisting of 45 ictal and 42 subclinical discharges were selected. Source localization was performed using the exact low-resolution electromagnetic tomography (eLORETA) algorithm, followed by FC based on the imaginary part of coherency. FC based on the thalamus as the seed of interest showed significant differences between ictal and subclinical GSWDs (p < 0.05). For delta (1–3 Hz) and alpha bands (8–12 Hz), the thalamus displayed stronger connectivity towards other brain regions for ictal GSWDs as compared to subclinical GSWDs. For delta band, the thalamus was strongly connected to the posterior cingulate cortex (PCC), precuneus, angular gyrus, supramarginal gyrus, parietal superior, and occipital mid-region for ictal GSWDs. The strong connections of the thalamus with other brain regions that are important for consciousness, and with components of the default mode network (DMN) suggest the severe impairment of consciousness in ictal GSWDs. However, for subclinical discharges, weaker connectivity between the thalamus and these brain regions may suggest the prevention of impairment of consciousness. This may benefit future therapeutic targets and improve the management of CAE patients.

Evaluating interhemispheric connectivity during midline object recognition using EEG.

Functional integration between two hemispheres is crucial for perceptual binding to occur when visual stimuli are presented in the midline of the visual field. Mima and colleagues (2001) showed using EEG that midline object recognition was associated with task-related decrease in alpha band power (alpha desynchronisation) and a transient increase in interhemispheric coherence. Our objective in the current study was to replicate the results of Mima et al. and to further evaluate interhemispheric effective connectivity during midline object recognition in source space. We recruited 11 healthy adult volunteers and recorded EEG from 64 channels while they performed a midline object recognition task. Task-related power and coherence were estimated in sensor and source spaces. Further, effective connectivity was evaluated using Granger causality. While we were able to replicate the alpha desynchronisation associated with midline object recognition, we could not replicate the coherence results of Mima et al. The data-driven approach that we employed in our study localised the source of alpha desynchronisation over the left occipito-temporal region. In the alpha band, we further observed significant increase in imaginary part of coherency between bilateral occipito-temporal regions during object recognition. Finally, Granger causality analysis between the left and right occipito-temporal regions provided an insight that even though there is bidirectional interaction, the left occipito-temporal region may be crucial for integrating the information necessary for object recognition. The significance of the current study lies in using high-density EEG and applying more appropriate and robust measures of connectivity as well as statistical analysis to validate and enhance our current knowledge on the neural basis of midline object recognition.

Impaired functional cortical networks in the theta frequency band of patients with post-traumatic stress disorder during auditory-cognitive processing.

Impaired cognitive function related to intrusive memories of traumatic experiences is the most noticeable characteristic of post-traumatic stress disorder (PTSD); nevertheless, the brain mechanism involved in the cognitive processing is still elusive. To improve the understanding of the neuropathology in PTSD patients, we investigated functional cortical networks that are based on graph theory, by using electroencephalogram (EEG). EEG signals, elicited by an auditory oddball paradigm, were recorded from 53 PTSD patients and 39 healthy controls (HCs). Source signals in 68 regions of interests were estimated using EEG data for each subject using minimum-norm estimation. Then, using source signals of each subject, time-frequency analysis was conducted, and a functional connectivity matrix was constructed using the imaginary part of coherence, which was used to evaluate three global-level (strength, clustering coefficient, and path length) and two nodal-level (strength and clustering coefficients) network indices in four frequency bands (theta, alpha, low-beta, and high-beta). The relationships between the network indices and symptoms were evaluated using Pearson’s correlation. Compared with HCs, PTSD patients showed significantly reduced spectral powers around P300 periods and significantly altered network indices (diminished strength and clustering coefficient, and prolonged path length) in theta frequency band. In addition, the nodal strengths and nodal clustering coefficients in theta band of PTSD patients were significantly reduced, compared with those of HCs, and the reduced nodal clustering coefficients in parieto-temporo-occipital regions had negative correlations with the symptom scores (Impact of Event Scale-Revises, Beck Depression Inventory, and Beck Anxiety Inventory). The characterization of this disrupted pattern improves the understanding of the neuropathophysiology underlying the impaired cognitive function in PTSD patients.

Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease.

The prevalence of dementia, including Alzheimer's disease (AD), is on the rise globally with screening and intervention of particular importance and benefit to those with limited access to healthcare. Electroencephalogram (EEG) is an inexpensive, scalable, and portable brain imaging technology that could deliver AD screening to those without local tertiary healthcare infrastructure. We study EEG recordings of subjects with sporadic mild cognitive impairment (MCI) and prodromal familial, early-onset, AD for the same working memory tasks using high- and low-density EEG, respectively. A challenge in detecting electrophysiological changes from EEG recordings is that noise and volume conduction effects are common and disruptive. It is known that the imaginary part of coherency (iCOH) can generate functional connectivity networks that mitigate against volume conduction, while also erasing true instantaneous activity (zero or π-phase). We aim to expose topological differences in these iCOH connectivity networks using a global network measure, eigenvector alignment (EA), shown to be robust to network alterations that emulate the erasure of connectivities by iCOH. Alignments assessed by EA capture the relationship between a pair of EEG channels from the similarity of their connectivity patterns. Significant alignments-from comparison with random null models-are seen to be consistent across frequency ranges (delta, theta, alpha, and beta) for the working memory tasks, where consistency of iCOH connectivities is also noted. For high-density EEG recordings, stark differences in the control and sporadic MCI results are observed with the control group demonstrating far more consistent alignments. Differences between the control and pre-dementia groupings are detected for significant correlation and iCOH connectivities, but only EA suggests a notable difference in network topology when comparing between subjects with sporadic MCI and prodromal familial AD. The consistency of alignments, across frequency ranges, provides a measure of confidence in EA's detection of topological structure, an important aspect that marks this approach as a promising direction for developing a reliable test for early onset AD.

P 80 How to avoid measurement of spurious inter-regional functional connectivity from EEG – a simulation study

Introduction: Aggregating statistical dependencies between multivariate time series is important to characterise functional connectivity (FC) between brain regions. However, it is still unclear how to reliably detect true FC from source-reconstructed M/EEG data. This study generates ground truth data and compares the performances of various pipelines that estimate directed FC (Time-Reversed Granger Causality TRGC 1 ) and undirected linear FC (imaginary part of coherency, absolute part of coherency and multivariate interaction measure MIM 2 ) between regions. Material and Methods: To simulate EEG-like sensor signals, we proceeded as follows: 1. Ground truth source activity was generated as white noise time series filtered in the alpha band. Between one and five pairs of sources interacted with certain time delays. 2. After adding individual 1/f noise, the source signals were projected to sensor space. 3. White sensor noise was added. Afterwards, sensor data were projected to source level by applying one of four tested inverse solutions: Dynamic imaging of coherent sources (DICS), Linearly Constrained Minimum Variance source projection (LCMV), eLORETA, and Champagne. All tested connectivity analysis pipelines calculate one connectivity score for every region combination. The pipelines‘ ability to detect the true interactions was evaluated by percentile rank. Results: The best-performing FC pipeline consists of the following steps: 1. Source projection with a beamformer inverse solution. 2. Principal component analysis within every region. 3. Selection of a fixed number of strongest principal components as basis for further analysis. 4. Calculation of the MIM for every region pair in case of undirected FC and calculation of the TRGC in case of directed FC. Worst performance was obtained with pipelines estimating undirected FC with the absolute value of coherency. DICS source projection resulted in good detection of undirected FC, but failure to detect the direction of FC with TRGC. Discussion: In this study, we tested several connectivity analysis pipelines that are used in the literature. Our simulation clearly shows that many of them do not detect true interactions reliably. To use the winning pipeline of this study could greatly increase the validity of future experimental connectivity studies.

FV 5 Striato-pallidal connectivity in oscillatory activity of patients with dystonia

Introduction: Dystonia is a network disorder in which aberrant signaling maybe associated with an imbalance between direct and indirect pathway activity. Invasive recordings from the internal pallidum (Globus pallidus internus, GPi), the major output structure of the basal ganglia, have described exaggerated oscillatory activity in the low frequency (LF) band (4-12 Hz). Oscillatory activity of the human striatum, as its major input structure, and its role in the generation and propagation of pathological LF activity is not well characterized. We hypothesize that i) the basal ganglia are functionally coupled within the LF band, ii) this connectivity is driven by the striatum and iii) LF activity could either be generated or amplified within the direct pathway. Patients & Methods: Rest recordings of local field potentials were performed in 11 patients undergoing pallidal deep brain stimulation (DBS) for cervical, segmental or generalized dystonia in the 1-7 days following surgery while DBS leads were externalized. The lead model used was DB-2201-45Bc, with 8 circular contacts spanning over 15.5 mm. DBS-leads were localized using the open-source software Lead-DBS and bipolar recordings from adjacent electrodes were assigned accordingly to either the putamen, Globus oallidus externus (GPe) or GPi. Power spectra were calculated for recordings from each structure, as well as the imaginary part of coherence (iCOH) as measure of functional connectivity and granger causality to assess directionality of coupling between structures in the LF band. Results: In all patients, one contact pair per hemisphere was localized in each structure of interest, namely the putamen, GPe and GPi. LF peaks could be detected in all structures of both hemispheres in all patients. LF- activity was coupled between all structures of one hemisphere (putamen-GPi, putamen-GPe, GPi-GPe) as assessed by iCOH and higher than coherence in shuffled data. Specifically the extent of coherence between putamen and GPi showed a positive correlation with motor symptom severity (R=0.63, P=.03). Directionality analyses were less consistent, with a trend towards striatum leading LF activity of internal and external pallidum that did not reach significance (P>0.05). Conclusion: We showed for the first time, that i) LF-activity can also be detected in the striatum of dystonia patients and ii) that striatum, GPe and GPi are coupled in this frequency band. However, only functional coupling in the LF-band between striatum and GPi significantly correlated with motor symptoms. This provides further evidence for the hypothesis that dystonia might be associated with aberrant signaling within the direct pathway. Regarding the directionality of LF-activity within the BG-loop, our preliminary results are less consistent and further analyses are needed. Taken together, these findings give insight to the pathophysiology of dystonia and the striatum as a potential target for neuromodulation.

Adaptive and Maladaptive Brain Functional Network Reorganization After Stroke in Hemianopia Patients: An Electroencephalogram-Tracking Study.

Objective: Hemianopia after occipital stroke is believed to be mainly due to local damage at or near the lesion site. However, magnetic resonance imaging studies suggest functional connectivity network (FCN) reorganization also in distant brain regions. Because it is unclear whether reorganization is adaptive or maladaptive, compensating for, or aggravating vision loss, we characterized FCNs electrophysiologically to explore local and global brain plasticity and correlated FCN reorganization with visual performance. Methods: Resting-state electroencephalography (EEG) was recorded in chronic, unilateral stroke patients and healthy age-matched controls (n = 24 each). This study was approved by the local ethics committee. The correlation of oscillating EEG activity was calculated with the imaginary part of coherence between pairs of regions of interest, and FCN graph theory metrics (degree, strength, clustering coefficient) were correlated with stimulus detection and reaction time. Results: Stroke brains showed altered FCNs in the alpha- and low beta-band in numerous occipital, temporal brain structures. On a global level, FCN had a less efficient network organization whereas on the local level node networks were reorganized especially in the intact hemisphere. Here, the occipital network was 58% more rigid (with a more "regular" network structure) whereas the temporal network was 32% more efficient (showing greater "small-worldness"), both of which correlated with worse or better visual processing, respectively. Conclusions: Occipital stroke is associated with both local and global FCN reorganization, but this can be both adaptive and maladaptive. We propose that the more "regular" FCN structure in the intact visual cortex indicates maladaptive plasticity, where less processing efficacy with reduced signal/noise ratio may cause the perceptual deficits in the intact visual field (VF). In contrast, reorganization in intact temporal brain regions is presumably adaptive, possibly supporting enhanced peripheral movement perception.

Brain Functional Connectivity in de novo Parkinson's Disease Patients Based on Clinical EEG.

In Parkinson's disease (PD), cortical–subcortical interplay plays a relevant role in affecting clinical performance. Functional MRI sequences described changes in functional connectivity at different stages of disease. Scarce are, instead, the investigations examining brain connectivity in patients with PD at early stages of disease. For this aim, here we analyzed the differences in functional connectivity between de novo, never treated, PD patients and healthy controls. The analyses were based upon custom-written scripts on the Matlab platform, combined with high-level functions of Fieldtrip, Brainstorm, and Brain Connectivity toolboxes. First, we proceeded to the spectral analysis of the EEG data in the five frequency bands (δ-θ-α-β-γ). Second, we calculated functional connectivity matrices based on both coherency (COH) and imaginary part of coherency (iCOH), in the δ-θ-α-β-γ frequency bands. Then, four network measures (density, transitivity, global efficiency, and assortativity) were computed in identified connectivity matrices. Finally, we compared the spectral density, functional connectivity matrices, and network measured between healthy controls and de novo PD patients through two-samples T-test. A total of 21 de novo PD patients and 20 healthy subjects were studied. No differences were observed in spectral analysis between the two groups, with the exception of the γ band where a significant increase in power density was found in PD patients. A reduced connectivity in the main EEG frequency bands (α-β frequency bands) was observed in PD patients compared to controls, while a hyperconnectivity was found in PD patients in γ band. Among the network measures, a reduced assortativity coefficient was found in de novo PD patients in α frequency band. Our results show the occurrence of early EEG functional connectivity alterations from the initial stages of PD. From this point of view, connectivity analysis may ease a better understanding of the complexity of PD physiopathology.

Looking through the windows: a study about the dependency of phase-coupling estimates on the data length.

Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain-computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes. We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio, the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (PLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence. Our findings show that, for a signal-to-noise-ratio of at least 10 dB, a data window that contains 5 to 8 cycles of the oscillation of interest (e.g. a 500-800ms window at 10Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required. The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.

Looking through the windows: a study about the dependency of phase-coupling estimates on the data length

Objective. Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain–computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes. Approach. We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio (SNR), the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (iPLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence. Main results. Our findings show that, for a SNR of at least 10 dB, a data window that contains 5–8 cycles of the oscillation of interest (e.g. a 500–800 ms window at 10 Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required. Significance. The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.

Resting‐state EEG gamma power and coherence might be an indicator of hyperexitability in patients with early‐onset Alzheimer’s disease

AbstractBackgroundEarly‐onset Alzheimer’s disease (EOAD) is characterized by disease onset age (&lt;65 years) and non‐amnestic symptoms at the initial stage. Disease course is different from the late‐onset AD (LOAD) in terms of pathology, neuropsychological manifestations and topography of electrophysiological changes. Present study aims to investigate resting state gamma power and connectivity changes in patients with EOAD compared to healthy controls.MethodThe study included 24 individuals with CSF‐proven EOAD (who met the NIA‐AA diagnostic criteria, McKhann et al., 2011) and demographically matched 24 healthy controls. Four minutes of eyes closed resting state EEG was recorded. Artifact‐free six‐second epochs were obtained. Power and coherency were measured for the total gamma (30–48Hz), gamma‐1 (30‐35Hz), gamma‐2 (36‐40Hz), and gamma‐3 (41‐48Hz). Imaginary part of coherency (ICoh) and maximum absolute values were calculated for intra‐hemispheric, inter‐hemispheric and midline electrode pairs.ResultFFT analysis showed higher gamma‐2 [F(1,46)=4,732,p=0.032], and gamma‐3 [F(1,46)=4,158,p=0.047] power in the EOAD group compared to controls. In the coherence analysis, the EOAD group had higher gamma‐2 inter‐hemispheric ICoh values than controls [F(1,46)=5.473;p=0.024]. The total intra‐hemispheric gamma values of the EOAD group were higher in the F8‐TP8 pair, but lower in the P8‐O2 and F4‐C4 pairs compared to controls [F(11,506)=1.901;p=0.037]. Intra‐hemispheric gamma‐2 values of the EOAD group were higher in the F8‐TP8 pair, but lower in the C3‐O1 pair [F(11,506)=2.207;p=0.031]. Midline gamma‐1 ICoh values were lower in the EOAD group than controls [F(1.46)=8.352;p=0.006].ConclusionTo our knowledge this is the first study to examine resting state gamma changes in individuals with CSF proven EOAD. Increased gamma power and connectivity in patients with LOAD (Başar et al., 2016,2017; van Deursen et al., 2008,2011) and Parkinson’s disease dementia (Pal et al., 2020) were reported. Increased power and connectivity were thought to reflect shared tau pathology between brain regions (Franzmeier et al., 2019). Literature provides evidence for hyperexcitability due to increased glutamate in AMPA and NMDA receptors in patients with AD (Um et al., 2012). Our results from CSF‐proven EOAD patients suggest that gamma power and connectivity might be good indicators of hyperexcitability in AD.

MEG Node Degree Differences in Patients with Focal Epilepsy vs. Controls-Influence of Experimental Conditions.

Drug-resistant epilepsy can be most limiting for patients, and surgery represents a viable therapy option. With the growing research on the human connectome and the evidence of epilepsy being a network disorder, connectivity analysis may be able to contribute to our understanding of epilepsy and may be potentially developed into clinical applications. In this magnetoencephalographic study, we determined the whole-brain node degree of connectivity levels in patients and controls. Resting-state activity was measured at five frequency bands in 15 healthy controls and 15 patients with focal epilepsy of different etiologies. The whole-brain all-to-all imaginary part of coherence in source space was then calculated. Node degree was determined and parcellated and was used for further statistical evaluation. In comparison to controls, we found a significantly higher overall node degree in patients with lesional and non-lesional epilepsy. Furthermore, we examined the conditions of high/reduced vigilance and open/closed eyes in controls, to analyze whether patient node degree levels can be achieved. We evaluated intraclass-correlation statistics (ICC) to evaluate the reproducibility. Connectivity and specifically node degree analysis could present new tools for one of the most common neurological diseases, with potential applications in epilepsy diagnostics.

Yes/No Classification of EEG data from CLIS patients.

The goal of this research is to evaluate the usability of new features to classify EEG data from several completely locked-in patients (CLIS), and eventually build a more reliable communication system for them. Patients in such state are completely paralyzed, preventing them to be able to talk, but they retain their cognitive abilities.The data were obtained from four CLIS patients and recorded during an auditory paradigm task during which they were asked yes/no questions. Spectral measures such as the relative power of δ, θ, α, β and γ frequency bands, spectral edge frequencies (SEF50 and SEF95), complexity measure obtained from Poincaré plots and connectivity measures such as the imaginary part of coherency and the weighted Symbolic Mutual Information (wSMI) were used as features. The data was classified using Random Forest and Support Vector Machine, two methods successfully used to classify mental states in both healthy subjects and patients. Additionally, two cases were studied. The first case uses data recorded when the patient is answering questions, while in the second case it also includes data recorded when the experimenter is asking the questions.The classification accuracy during training varies between 51.73 to 67.72% in the first case, and from 50.41 to 67.94% for the second case. Overall, wSMI with a time lag of 64 ms gave the best classification accuracy and in general, Random Forest appears to be the best classification method.Clinical relevance This case study investigates the usability of new features based on EEG complexity and connectivity to classify CLIS patients brain signal, what results in a further step toward the demand of more effective EEG-based Brain-Computer Interface communication systems for CLIS patients.

Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study

ObjectiveChildhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs. MethodsTwelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1–30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks. ResultsDelta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs. ConclusionUsing spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring. SignificanceIdentifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.