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Exploring non-stationarity patterns in schizophrenia: neural reorganization abnormalities in the alpha band

OBJECTIVE The aim of this paper was to characterize brain non-stationarity during an auditory oddball task in schizophrenia (SCH). The level of non-stationarity was measured in the baseline and response windows of relevant tones in SCH patients and healthy controls. APPROACH Event-related potentials were recorded from 28 SCH patients and 51 controls. Non-stationarity was estimated in the conventional electroencephalography frequency bands by means of Kullback-Leibler divergence (KLD). Relative power (RP) was also computed to assess a possible complementarity with KLD. MAIN RESULTS Results showed a widespread statistically significant increase in the level of non-stationarity from baseline to response in all frequency bands for both groups. Statistically significant differences in non-stationarity were found between SCH patients and controls in beta-2 and in the alpha band. SCH patients showed more non-stationarity in the left parieto-occipital region during the baseline window in the beta-2 band. A leave-one-out cross validation classification study with feature selection based on binary stepwise logistic regression to discriminate between SCH patients and controls provided a positive predictive value of 72.73% and negative predictive value of 78.95%. SIGNIFICANCE KLD can characterize transient neural reorganization during an attentional task in response to novelty and relevance. Our findings suggest anomalous reorganization of neural dynamics in SCH during an oddball task. The abnormal frequency-dependent modulation found in SCH patients during relevant tones is in agreement with the hypothesis of aberrant salience detection in SCH. The increase in non-stationarity in the alpha band during the active task supports the notion that this band is involved in top-down processing. The baseline differences in the beta-2 band suggest that hyperactivation of the default mode network during attention tasks may be related to SCH symptoms. Furthermore, the classification improved when features from both KLD and RP were used, supporting the idea that these measures can be complementary.

Alterations of Effective Connectivity Patterns in Mild Cognitive Impairment: An MEG Study

Neuroimaging techniques have demonstrated over the years their ability to characterize the brain abnormalities associated with different neurodegenerative diseases. Among all these techniques, magnetoencephalography (MEG) stands out by its high temporal resolution and noninvasiveness. The aim of the present study is to explore the coupling patterns of resting-state MEG activity in subjects with mild cognitive impairment (MCI). To achieve this goal, five minutes of spontaneous MEG activity were acquired with a 148-channel whole-head magnetometer from 18 MCI patients and 26 healthy controls. Inter-channel relationships were investigated by means of two complementary coupling measures: coherence and Granger causality. Coherence is a classical method of functional connectivity, while Granger causality quantifies effective (or causal) connectivity. Both measures were calculated in the five conventional frequency bands: delta (δ, 1-4 Hz), theta (θ, 4-8 Hz), alpha (α, 8-13 Hz), beta (β, 13-30 Hz), and gamma (γ, 30-45 Hz). Our results showed that connectivity values were lower for MCI patients than for controls in all frequency bands. However, only Granger causality revealed statistically significant differences between groups (p-values < 0.05, FDR corrected Mann-Whitney U-test), mainly in the beta band. Our results support the role of MCI as a disconnection syndrome, which elicits early alterations in effective connectivity patterns. These findings can be helpful to identify the neural substrates involved in prodromal stages of dementia.

Spectral Regression Kernel Discriminant Analysis for P300 Speller Based Brain-Computer Interfaces

This study proposes a novel classification algorithm for enhancing the performance of online P300 Speller based Brain-Computer Interface (BCI) applications. The key element of the algorithm is an ensemble of spectral regression kernel discriminant analysis (SRKDA) classifiers, an optimized version of the well-known KDA. The method was tested with the III BCI Competition dataset IIa and results were compared with LDA and the winner of the competition. Reached accuracies outperforms both of them for 5 and 15 sequences, making the proposed method a suitable alternative for this kind of applications.

Analysis of the non-stationarity of neural activity during an auditory oddball task in schizophrenia

The aim of this study was to characterize brain dynamics during an auditory oddball task. For this purpose, a measure of the non-stationarity of a given time-frequency representation (TFR) was applied to electroencephalographic (EEG) signals. EEG activity was acquired from 20 schizophrenic (SCH) patients and 20 healthy controls while they underwent a three-stimulus auditory oddball task. The Degree of Stationarity (DS), a measure of the non-stationarity of the TFR, was computed using the continuous wavelet transform. DS was calculated for both the baseline [-300 0] ms and active task [150 550] ms windows of a P300 auditory oddball task. Results showed a statistically significant increase (p<;0.05) in non-stationarity for controls during the cognitive task in the central region, while less widespread statistically significant differences were obtained for SCH patients, especially in the beta-2 and gamma bands. Our findings support the relevance of DS as a means to study cerebral processing in SCH. Furthermore, the lack of statistically significant changes in DS for SCH patients suggests an abnormal reorganization of neural dynamics during an oddball task.

Characterizing Non-stationarity in Alzheimer’s Disease and Mild Cognitive Impairment by Means of Kullback-Leibler Divergence

The aim of this study was to characterize the non-stationarity level of resting-state EEG in patients with dementia due to Alzheimer’s disease (AD), subjects with mild cognitive impairment (MCI) and healthy controls. A frequency-dependent implementation of the Kullback-Leibler divergence was used to characterize non-stationarity patterns. The results showed a statistically significant increase in non-stationarity for AD patients with respect to controls in the 1–70 Hz frequency range, as well as a less pronounced increase for MCI subjects with respect to controls. These results suggest that EEG activity during short time windows consists of more structured oscillations than that of AD patients or MCI subjects.

Analysis of Electroencephalographic Dynamic Functional Connectivity in Alzheimer’s Disease

The aim of this study was to characterize the dynamic functional connectivity of resting-state electroencephalographic (EEG) activity in Alzheimer’s disease (AD). The magnitude squared coherence (MSCOH) of 50 patients with dementia due to AD and 28 cognitively healthy controls was computed. MSCOH was estimated in epochs of 60 s subdivided in overlapping windows of different lengths (1, 2, 3, 5 and 10 s; 50% overlap). The effect of epoch length was tested on MSCOH and it was found that MSCOH stabilized at a window length of 3 s. We tested whether the MSCOH fluctuations observed reflected actual changes in functional connectivity by means of surrogate data testing, with the standard deviation of MSCOH chosen as the test statistic. The results showed that the variability of the measure could be due to dynamic functional connectivity. Furthermore, a significant reduction in the dynamic MSCOH connectivity of AD patients compared to controls was found in the delta (0–4 Hz) and beta-1 (13–30 Hz) bands. This indicated that AD patients show lesser variation in neural connectivity during resting state. Finally, a correlation between relative power and standard deviation was found, suggesting that an increase/peak in power spectrum could be a pre-requisite for dynamic functional connectivity in a specific frequency band.

Deficits of entropy modulation in schizophrenia are predicted by functional connectivity strength in the theta band and structural clustering

Spectral entropy (SE) allows comparing task-related modulation of electroencephalogram (EEG) between patients and controls, i.e. spectral changes of the EEG associated to task performance. A SE modulation deficit has been replicated in different schizophrenia samples. To investigate the underpinnings of SE modulation deficits in schizophrenia, we applied graph-theory to EEG recordings during a P300 task and fractional anisotropy (FA) data from diffusion tensor imaging in 48 patients (23 first episodes) and 87 healthy controls. Functional connectivity was assessed from phase-locking values among sensors in the theta band, and structural connectivity was based on FA values for the tracts connecting pairs of regions. From those data, averaged clustering coefficient (CLC), characteristic path-length (PL) and connectivity strength (CS, also known as density) were calculated for both functional and structural networks. The corresponding functional modulation values were calculated as the difference in SE and CLC, PL and CS between the pre-stimulus and response windows during the task. The results revealed a higher functional CS in the pre-stimulus window in patients, predictive of smaller modulation of SE in this group. The amount of increase in theta CS from pre-stimulus to response related to SE modulation in patients and controls. Structural CLC was associated with SE modulation in the patients. SE modulation was predictive of negative symptoms, whereas CLC and PL modulation was associated with cognitive performance in the patients. These results support that a hyperactive functional connectivity and/or structural connective deficits in the patients hamper the dynamical modulation of connectivity underlying cognition.

Analysis of Information Flux in Alzheimer’s Disease and Mild Cognitive Impairment by Means of Graph-Theory Parameters

The aim of this study is to evaluate the changes that Alzheimer’s disease (AD) and mild cognitive impairment (MCI) cause in the neural patterns of information flow and in the brain network properties. For this purpose, phase-slope index (PSI) was applied to spontaneous electroencephalographic activity from 32 AD patients, 10 MCI subjects and 18 cognitively healthy controls. Then, three network parameters were calculated: average node degree (ND), cluster coefficient (CC), and characteristic path length (PL). Our results showed that information flux values were lower for AD and MCI subjects, compared to controls. Additionally, significantly lower ND and higher PL values were obtained in AD group, compared with MCI and controls in alpha frequency band. These findings support the idea of disconnection syndrome in AD and revealed less efficient brain organization as the disease progresses.

Relations between structural and EEG-based graph metrics in healthy controls and schizophrenia patients

Our aim was to assess structural and functional networks in schizophrenia patients; and the possible prediction of the latter based on the former. The possible dependence of functional network properties on structural alterations has not been analyzed in schizophrenia. We applied averaged path‐length (PL), clustering coefficient, and density (D) measurements to data from diffusion magnetic resonance and electroencephalography in 39 schizophrenia patients and 79 controls. Functional data were collected for the global and theta frequency bands during an odd‐ball task, prior to stimulus delivery and at the corresponding processing window. Connectivity matrices were constructed from tractography and registered cortical segmentations (structural) and phase‐locking values (functional). Both groups showed a significant electroencephalographic task‐related modulation (change between prestimulus and response windows) in the global and theta bands. Patients showed larger structural PL and prestimulus density in the global and theta bands, and lower PL task‐related modulation in the theta band. Structural network values predicted prestimulus global band values in controls and global band task‐related modulation in patients. Abnormal functional values found in patients (prestimulus density in the global and theta bands and task‐related modulation in the theta band) were not predicted by structural data in this group. Structural and functional network abnormalities respectively predicted cognitive performance and positive symptoms in patients. Taken together, the alterations in the structural and functional theta networks in the patients and the lack of significant relations between these alterations, suggest that these types of network abnormalities exist in different groups of schizophrenia patients.

Event-Related Phase-Amplitude Coupling: a comparative study

The aim of this study was to explore the coupling among neural oscillations in different frequency bands using two approaches: conventional phase-amplitude coupling (PAC) and a novel event-related PAC. Both measures were applied to the electroencephalographic activity from 20 healthy volunteers. The results showed that the phase of alpha band modulated gamma power. Event-related PAC measures the coupling between frequency rhythms without losing of temporal resolution. Therefore, it may provide further insights into the characterization of brain dynamics.