Neural complexity in patients with poststroke depression: A resting EEG study

From the brain's encoding of input dynamics to its behavior: neural dynamics shape bias in decision making

The human brain is tightly connected to the individual's environment and its input dynamics. How the dynamics of periodic environmental stimuli influence neural activity and subsequent behavior via neural entrainment or alignment is not fully clear yet, though. This study explores how periodic environmental stimuli influence neural activity and behavior. EEG data was collected during a Go-NoGo task with a periodic intertrial interval (ITI) of 1.3 s (0.769 Hz). Results showed that the task's temporal structure increased power spectrum activity at 0.769 Hz, which showed high intersubject variability. Higher task-periodicity effects were linked to stronger phase-based intertrial coherence (ITC) and reduced neural complexity, as measured by lower Lempel-Ziv Complexity (LZC). Additionally, higher periodicity in the power spectrum correlated with faster reaction times and stronger response bias. We conclude that the encoding of the inputs' dynamics into the brains power spectrum shapes subsequent behavior, e.g., RT and response bias, through reducing neural complexity.

EEG microstate as a biomarker of post-stroke depression with acupuncture treatment

Background

Post-stroke depression (PSD) is a prevalent psychiatric complication among stroke survivors. The PSD researches focus on pathogenesis, new treatment methods and efficacy prediction. This study explored the electroencephalography (EEG) microstates in PSD and assessed their changes after acupuncture treatment, aiming to find the biological characteristics and the predictors of treatment efficacy of PSD.

Methods

A 64-channel resting EEG data was collected from 70 PSD patients (PSD group) and 40 healthy controls (HC group) to explore the neuro-electrophysiological mechanism of PSD. The PSD patients received 6 weeks of acupuncture treatment. EEG data was collected from 60 PSD patients after acupuncture treatment (MA group) to verify whether acupuncture had a modulating effect on abnormal EEG microstates. Finally, the MA group was divided into two groups: the remission prediction group (RP group) and the non-remission prediction group (NRP group) according to the 24-Item Hamilton Depression Scale (HAMD-24) reduction rate. A prediction model for acupuncture treatment was established by baseline EEG microstates.

Results

The duration of microstate D along with the occurrence and contribution of microstate C were reduced in PSD patients. Acupuncture treatment partially normalized abnormal EEG microstates in PSD patients. Baseline EEG microstates predicted the efficacy of acupuncture treatment with an area under the curve (AUC) of 0.964.

Conclusion

This study provides a novel viewpoint on the neurophysiological mechanisms of PSD and emphasizes the potential of EEG microstates as a functional biomarker. Additionally, we anticipated the therapeutic outcomes of acupuncture by analyzing the baseline microstates, which holds significant practical implication for the PSD treatment.

The effect of aromatherapy on post-stroke depression: study protocol for a pilot randomized controlled trial

Background

Post-stroke depression (PSD) is a prevalent psychiatric disorder affecting about one-third of stroke survivors, significantly hindering recovery and quality of life. PSD also imposes a substantial burden on caregivers and healthcare systems. Aromatherapy has shown promise in alleviating depression, anxiety, and sleep disorders. This pilot randomized controlled trial aims to assess the feasibility and preliminary efficacy of mixed herb aromatherapy in treating PSD. Feasibility outcomes encompass recruitment, intervention adherence, assessment completion and safety assessment. Secondary outcomes include evaluations of depression, anxiety, cognitive function, sleep quality, quality of life, and brain function using EEG and fNIRS.

Methods

This single-blind pilot randomized controlled trial will be conducted at the Second Rehabilitation Hospital of Shanghai, enrolling ninety-nine post-stroke patients with PSD. Participants will be randomized into three groups: a Non-Active Control Group receiving standardized rehabilitation therapy, a CBT Group receiving conventional rehabilitation with bi-weekly CBT sessions, and an Aromatherapy Group receiving conventional rehabilitation with daily aromatic inhalation sessions. Interventions will last for four weeks, with efficacy assessed at baseline, post-intervention, and one month post-intervention. Rating scales will be used to measure changes in depression, sleep quality, cognitive function, and quality of life. EEG and fNIRS will specifically be used to measure changes in cerebral cortex activity and their correlations with depression. Feasibility will be evaluated through recruitment, intervention adherence, assessment completion and safety assessment.

Discussion

This pilot study highlights the potential of mixed herb aromatherapy inhalation for treating PSD, addressing limitations of CBT by promoting self-management. While demonstrating feasibility through recruitment, adherence, assessment completion and safety assessment, the study also acknowledges limitations such as unequal intervention times, the lack of physical function data. And the use of culturally relevant plant powders may enhance compliance but limits generalizability. Despite these constraints, the study provides valuable preliminary data and insights into the mechanisms of aromatherapy, encouraging further research and development of effective PSD treatments.

Major depressive disorder recognition by quantifying EEG signal complexity using proposed APLZC and AWPLZC

BACKGROUND

Seeking objective quantitative indicators is important for accurately recognizing major depressive disorder (MDD). Lempel-Ziv complexity (LZC), employed to characterize neurological disorders, faces limitations in tracking dynamic changes in EEG signals due to defects in the coarse-graining process, hindering its precision for MDD objective quantitative indicators.

METHODS

This work proposed Adaptive Permutation Lempel-Ziv Complexity (APLZC) and Adaptive Weighted Permutation Lempel-Ziv Complexity (AWPLZC) algorithms by refining the coarse-graining process and introducing weight factors to effectively improve the precision of LZC in characterizing EEGs and further distinguish MDD patients better. APLZC incorporated the ordinal pattern, while False Nearest Neighbor and Mutual Information algorithms were introduced to determine and adjust key parameters adaptively. Furthermore, we proposed AWPLZC by assigning different weights to each pattern based on APLZC. Thirty MDD patients and 30 healthy controls (HCs) were recruited and their 64-channel resting EEG signals were collected. The complexities of gamma oscillations were then separately computed using LZC, APLZC, and AWPLZC algorithms. Subsequently, a multi-channel adaptive K-nearest neighbor model was constructed for identifying MDD patients and HCs.

RESULTS

LZC, APLZC, and AWPLZC algorithms achieved accuracy rates of 78.29 %, 90.32 %, and 95.13 %, respectively. Sensitivities reached 67.96 %, 85.04 %, and 98.86 %, while specificities were 88.62 %, 95.35 %, and 89.92 %, respectively. Notably, AWPLZC achieved the best performance in accuracy and sensitivity, with a specificity limitation.

LIMITATION

The sample size is relatively small.

CONCLUSION

APLZC and AWPLZC algorithms, particularly AWPLZC, demonstrate superior effectiveness in differentiating MDD patients from HCs compared with LZC. These findings hold significant clinical implications for MDD diagnosis.

Fractal Phototherapy in Maximizing Retina and Brain Plasticity

The neuroplasticity potential is reduced with aging and impairs during neurodegenerative diseases and brain and visual system injuries. This limits the brain's capacity to repair the structure and dynamics of its activity after lesions. Maximization of neuroplasticity is necessary to provide the maximal CNS response to therapeutic intervention and adaptive reorganization of neuronal networks in patients with degenerative pathology and traumatic injury to restore the functional activity of the brain and retina.Considering the fractal geometry and dynamics of the healthy brain and the loss of fractality in neurodegenerative pathology, we suggest that the application of self-similar visual signals with a fractal temporal structure in the stimulation therapy can reactivate the adaptive neuroplasticity and enhance the effectiveness of neurorehabilitation. This proposition was tested in the recent studies. Patients with glaucoma had a statistically significant positive effect of fractal photic therapy on light sensitivity and the perimetric MD index, which shows that methods of fractal stimulation can be a novel nonpharmacological approach to neuroprotective therapy and neurorehabilitation. In healthy rabbits, it was demonstrated that a long-term course of photostimulation with fractal signals does not harm the electroretinogram (ERG) and retina structure. Rabbits with modeled retinal atrophy showed better dynamics of the ERG restoration during daily stimulation therapy for a week in comparison with the controls. Positive changes in the retinal function can indirectly suggest the activation of its adaptive plasticity and the high potential of stimulation therapy with fractal visual stimuli in a nonpharmacological neurorehabilitation, which requires further study.

Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Mental disorders, especially depression, have become a rising problem in modern society. The development of methods and markers for the early detection of mental disorders is an actual problem. Psychological questionnaires are the only tools for evaluating the symptoms of mental disorders in clinical practice today. The electroencephalography (EEG) based non-invasive and cost-effective method seems feasible for the early detection of depression in occupational and family medicine centers and personal monitoring. The reliability of the EEG markers in the early detection of depression assumes their high temporal stability and correlation with the scores of depression questionnaires. The study was been performed on 17 healthy people over three years. Two hypotheses have been evaluated in the current study: first, the temporal stability of EEG markers is close to the stability of the scores of depression questionnaires, and second, EEG markers and depression questionnaires' scores are not correlated in healthy people. The results of the performed study support both hypotheses: the temporal stability of EEG markers is high and close to the stability of depression questionnaires scores and the correlation between the EEG markers and depression questionnaires scores is not detected in healthy people. The results of the current study contribute to the interpretation of results in depression EEG studies and to the feasibility of EEG markers in the detection of depression.

Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline

This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process.

Relationship Between Abnormalities in Resting-State Quantitative Electroencephalogram Patterns and Poststroke Depression

PURPOSE

Spectral power analysis of quantitative EEG has gained popularity in the assessment of depression, but findings across studies concerning poststroke depression (PSD) have been inconsistent. The goal of this study was to determine the extent to which abnormalities in quantitative EEG differentiate patients with PSD from poststroke nondepressed (PSND) subjects.

METHODS

Resting-state EEG signals of 34 participants (11 patients with PSD and 23 PSND subjects) were recorded, and then the spectral power analysis for six frequency bands (alpha1, alpha2, beta1, beta2, delta, and theta) was conducted at 16 electrodes. Pearson linear correlation analysis was used to investigate the association between depression severity measured with the Hamilton Depression Rating Scale (HDRS) total score and absolute power values. In addition, receiver operating characteristic curves were used to assess the sensitivity and specificity of quantitative EEG in discriminating PSD.

RESULTS

In comparison with PSND patients, PSD patients showed significantly higher alpha1 power in left temporal region and alpha2 power at left frontal pole. Higher theta power in central, temporal, and occipital regions was observed in patients with PSD. The results of Pearson linear correlation analysis showed significant association between HDRS total score and the absolute alpha1 power in frontal, temporal, and parietal regions.

CONCLUSIONS

Absolute powers of alpha and theta bands significantly distinguish between PSD patients and PSND subjects. Besides, absolute alpha1 power is positively associated with the severity of depression.

Post-stroke depression: Chaos to exposition

Cerebral ischemia contributes to significant disabilities worldwide, impairing cognitive function and motor coordination in affected individuals. Stroke has severe neuropsychological outcomes, the major one being a stroke. Stroke survivors begin to show symptoms of depression within a few months of the incidence that overtime progresses to become a long-term ailment. As the pathophysiology for the progression of the disease is multifactorial and complex, it limits the understanding of the disease mechanism completely. Meta-analyses and randomized clinical trials have shown that intervening early with tricyclic antidepressants and selective serotonin receptor inhibitors can be effective. However, these pharmacotherapies possess several limitations that have given rise to newer approaches such as brain stimulation, psychotherapy and rehabilitation therapy, which in today's time are gaining attention for their beneficial results in post-stroke depression (PSD). The present review highlights numerous factors like lesion location, inflammatory mediators and genetic abnormalities that play a crucial role in the development of depression in stroke patients. Further, we have also discussed various mechanisms involved in post-stroke depression (PSD) and strategies for early detection and diagnosis using biomarkers that may revolutionize treatment for the affected population. Towards the end, along with the preclinical scenario, we have also discussed the various treatment approaches like pharmacotherapy, traditional medicines, psychotherapy, electrical stimulation and microRNAs being utilized for effectively managing PSD.

Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review

Background

Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use.

Objective

This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models.

Methods

To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology.

Results

We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism.

Conclusions

This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.

Consistency of local activation parameters at sensor- and source-level in neural signals

OBJECTIVE

Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms have been proposed; however, they are not exempt from limitations and usually involve time-consuming procedures. Connectivity and network-based M/EEG analyses have been found to be affected by field spread and volume conduction effects; nevertheless, the influence of the aforementioned effects on widely used local activation parameters has not been assessed yet. The goal of this study is to evaluate the consistency of various local activation parameters when they are computed at sensor- and source-level.

APPROACH

Six spectral (relative power, median frequency, and individual alpha frequency) and non-linear parameters (Lempel-Ziv complexity, sample entropy, and central tendency measure) are computed from M/EEG signals at sensor- and source-level using four source inversion methods: weighted minimum norm estimate (wMNE), standardised low-resolution brain electromagnetic tomography (sLORETA), linear constrained minimum variance (LCMV), and dynamical statistical parametric mapping (dSPM).

MAIN RESULTS

Our results show that the spectral and non-linear parameters yield similar results at sensor- and source-level, showing high correlation values between them for all the source inversion methods evaluated and both modalities of signal, EEG and MEG. Furthermore, the correlation values remain high when performing coarse-grained spatial analyses.

SIGNIFICANCE

To the best of our knowledge, this is the first study analysing how field spread and volume conduction effects impact on local activation parameters computed from resting-state neural activity. Our findings evidence that local activation parameters are robust against field spread and volume conduction effects and provide equivalent information at sensor- and source-level even when performing regional analyses.

Factors Affecting Quality of Life among Post-Stroke Patients in the Sub-Himalayan Region

Background  Stroke is one of the most debilitating conditions contributing to significant disability and death globally. Identifying risk factors for quality of life (QoL) will enable to improve home-based rehabilitation in post-stroke phase.

Objective  This study was aimed to identify the risk factors of QoL in stroke patients in the sub-Himalayan region.

Materials and Methods  A cross-sectional hospital-based study assessed the QoL among stroke patients within a week after the onset of acute stroke and then re-evaluated at 3 months. World Health Organization QoL-BREF, Beck Depression Inventory, the Barthel Index, and Montreal Cognitive Assessment (MOCA) were used to seek data on QoL, depression, cognitive, and functional dependence status, respectively. Appropriate statistics were used to compute the results.

Results  In total, 129 stroke patients recruited, out of which 102 returned to a 3-month follow-up. QoL, MOCA, disability index, and depression score were compared using Wilcoxon Singed-rank test. In multivariate analysis, depression and disability together predicted 60% of the variance for physical QoL ( p < 0.0001). Similarly, poststroke depression and disability together predicted 61% of the variance for psychological QoL ( p < 0.0001) in stroke patients.

Conclusion  Findings indicated that depression and disability are leading risk factors of QoL in stroke patients. Early identification of poststroke depression and functional dependence status is, therefore, essential to devise screening procedure and to develop targeted intervention to improve rehabilitation outcomes.

Quantitative and Qualitative EEG as a Prediction Tool for Outcome and Complications in Acute Stroke Patients

Currently, the relevance of EEG measurements in acute stroke patients is considered low in clinical practice. However, recent studies on the predictive value of EEG measurements after stroke for various outcomes may increase the role of EEG in patients with stroke. We aimed to review the current literature on the utility of EEG measurements after stroke as a tool to predict outcome and complications, focusing on studies in which the EEG measurement was performed in the acute phase after the event and in which long-term outcome measures were reported. In our literature review, we identified 4 different outcome measures (functional outcome, mortality, development of post-stroke cognitive decline, and development of post-stroke epilepsy) where studies on the utility of acute EEG measurements exist. There is a large body of evidence for the prediction of functional outcome, in which a multitude of associated quantitative and qualitative EEG parameters are described. In contrast, only few studies focus on mortality as outcome parameter. We found studies of high methodical quality on the prediction of post-stroke cognitive decline, though the number of patients in these studies often was small. The role of EEG as a prediction tool for seizures and epilepsy after stroke could increase after a recently published study, especially if its result can be incorporated into already existing post-stroke epilepsy prediction tools. In summary, EEG is useful for the prediction of functional outcome, mortality, development of post-stroke cognitive decline and epilepsy, even though there is a discrepancy between the large amount of studies on EEG in acute stroke patients and its underuse in clinical practice.

Altered Topology of the Structural Brain Network in Patients With Post-stroke Depression

There is a pressing need to further our understanding of the mechanisms underlying the depression symptoms in patients with post-stroke depression (PSD) in order to inform targeted therapeutic approaches. While previous research has demonstrated a reorganization in the functional brain network of PSD, it remains uncertain whether, or not it also occurs in the structural brain network. We therefore aim to investigate the structural brain network of patients with PSD as compared to post-stroke non-depression (PSND) patients. In addition, our research considers the relationship between network metrics and functional measurements. Thirty-one PSD patients and twenty-three PSND patients were recruited. All patients underwent MRI and functional assessments, including the Barthel index, mini-mental state examination (MMSE), and Hamilton depression rating scale (HAMD). Diffusion tensor imaging was used to construct the structural brain network and to conduct the subsequent graph theoretical analysis. Network measures were computed and compared between PSD and PSND patients. Associations between functional assessments and network measures were studied as well. We successfully detected increased global and local efficiency in patients with PSD. Regions with disrupted local connections were located primarily in the cognitive and limbic systems. More importantly, PSD patients' global and regional network measures were associated with depression severity, as measured by HAMD. These findings suggest that disrupted global and local network topologies might contribute to PSD patients' depression symptoms. Therefore, connectome-based network measures could be potential bio-markers for evaluating stroke patients' depression levels.

How Physical Activities Affect Mental Fatigue Based on EEG Energy, Connectivity, and Complexity

Many studies have verified that there is an interaction between physical activities and mental fatigue. However, few studies are focused on the effect of physical activities on mental fatigue. This study was to analyze the states of mental fatigue based on electroencephalography (EEG) and investigate how physical activities affect mental fatigue. Fourteen healthy participants participated in an experiment including a 2-back mental task (the control) and the same mental task with cycling simultaneously (physical-mental task). Each experiment consisted of three 20 min fatigue-inducing sessions repeatedly (mental fatigue for mental tasks or mental fatigue plus physical activities for physical-mental tasks). During the evaluation sessions (before and after the fatigue-inducing sessions), the states of the participants were assessed by EEG parameters. Wavelet Packet Energy (WPE), Spectral Coherence Value (SCV), and Lempel-Ziv Complexity (LZC) were used to indicate mental fatigue from the perspectives of activation, functional connectivity, and complexity of the brain. The indices are the beta band energy E_β, the energy ratio E_α/β, inter-hemispheric SCV of beta band SCV_β and LZC. The statistical analysis shows that mental fatigue was detected by E_β, E_α/β, SCV_β, and LZC in physical-mental task. The slopes of the linear fit on these indices verified that the mental fatigue increased more fast during physical-mental task. It is concluded form the result that physical activities can enhance the mental fatigue and speed up the fatigue process based on brain activation, functional connection, and complexity. This result differs from the traditional opinion that physical activities have no influence on mental fatigue, and finds that physical activities can increase mental fatigue. This finding helps fatigue management through exercise instruction.

Poststroke Depression Biomarkers: A Narrative Review

Poststroke depression (PSD) is the most prevalent psychiatric disorder after stroke, which is independently correlated with negative clinical outcome. The identification of specific biomarkers could help to increase the sensitivity of PSD diagnosis and elucidate its pathophysiological mechanisms. The aim of current study was to review and summarize literature exploring potential biomarkers for PSD diagnosis. The PubMed database was searched for papers published in English from October 1977 to December 2017, 90 of which met inclusion criteria for clinical studies related to PSD biomarkers. PSD biomarkers were subdivided into neuroimaging, molecular, and neurophysiological. Some of them could be recommended to support PSD diagnosing. According to the data, lesions affecting the frontal-subcortical circles of mood regulation (prefrontal cortex, basal nuclei, and thalamus) predominantly in the left hemisphere can be considered as neuroimaging markers and predictors for PSD for at least 1 year after stroke. Additional pontine and lobar cerebral microbleeds in acute stroke patients, as well as severe microvascular lesions of the brain, increase the likelihood of PSD. The following molecular candidates can help to differentiate PSD patients from non-depressed stroke subjects: decreased serum BDNF concentrations; increased early markers of inflammation (high-sensitivity C-reactive protein, ferritin, neopterin, and glutamate), serum pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, IL-18, IFN-γ), as well as pro-inflammatory/anti-inflammatory ratios (TNF-α/IL-10, IL-1β/IL-10, IL-6/IL-10, IL-18/IL-10, IFN-γ/IL-10); lowered complement expression; decreased serum vitamin D levels; hypercortisolemia and blunted cortisol awakening response; S/S 5-HTTLPR, STin2 9/12, and 12/12 genotypes of the serotonin transporter gene SLC6A4, 5-HTR2a 1438 A/A, and BDNF met/met genotypes; higher SLC6A4 promoter and BDNF promoter methylation status. Neurophysiological markers of PSD, that reflect a violation of perception and cognitive processing, are the elongation of the latency of N200, P300, and N400, as well as the decrease in the P300 and N400 amplitude of the event-related potentials. The selected panel of biomarkers may be useful for paraclinical underpinning of PSD diagnosis, clarifying various aspects of its multifactorial pathogenesis, optimizing therapeutic interventions, and assessing treatment effectiveness.

Electrophysiological changes in poststroke subjects with depressed mood: A quantitative EEG study

BACKGROUND

We aimed to explore the electrophysiological changes in poststroke subjects with depressed mood.

METHODS

Resting-state electroencephalogram (EEG) signals of 16 electrodes in 35 poststroke depressed, 24 poststroke nondepressed, and 35 age-matched healthy control subjects were analyzed by means of spectral power analysis, a quantitative EEG measurement of different frequency bands. The relationship among depressed mood, functional status, lesion side, and poststroke time was assessed by using variance and Spearman correlation analysis. Multiple analysis of variance was used to compare the differences among the 3 groups. Binary logistic regression analysis was used to establish a regression model to predict depressed mood in stroke subjects and to explore the association between depression and EEG band power. Receiver operating characteristic curves were used to estimate the ability of spectral power selected by binary logistic regression to indicate depressed mood in stroke subjects.

RESULTS

We found that the hemisphere in which the lesion was located and the time since stroke onset had no effect on depressed mood. Only the patient's functional status was related to emotional symptoms. Quantitative EEG analysis revealed increased delta, theta, and beta2 power in stroke subjects with depressed mood, particularly in temporal regions. The theta and beta2 power in the right temporal area were shown to be highly sensitive to depressed mood, and these parameters showed good discriminatory ability for depressed subjects following stroke.

CONCLUSION

Depressed mood after stroke is associated with functional status. Quantitative EEG parameters may be a useful tool in timely screening for depressed mood after stroke.

Multiscale Information Transfer in Functional Corticomuscular Coupling Estimation Following Stroke: A Pilot Study

Recently, functional corticomuscular coupling (FCMC) between the cortex and the contralateral muscle has been used to evaluate motor function after stroke. As we know, the motor-control system is a closed-loop system that is regulated by complex self-regulating and interactive mechanisms which operate in multiple spatial and temporal scales. Multiscale analysis can represent the inherent complexity. However, previous studies in FCMC for stroke patients mainly focused on the coupling strength in single-time scale, without considering the changes of the inherently directional and multiscale properties in sensorimotor systems. In this paper, a multiscale-causal model, named multiscale transfer entropy, was used to quantify the functional connection between electroencephalogram over the scalp and electromyogram from the flexor digitorum superficialis (FDS) recorded simultaneously during steady-state grip task in eight stroke patients and eight healthy controls. Our results showed that healthy controls exhibited higher coupling when the scale reached up to about 12, and the FCMC in descending direction was stronger at certain scales (1, 7, 12, and 14) than that in ascending direction. Further analysis showed these multi-time scale characteristics mainly focused on the beta1 band at scale 11 and beta2 band at scale 9, 11, 13, and 15. Compared to controls, the multiscale properties of the FCMC for stroke were changed, the strengths in both directions were reduced, and the gaps between the descending and ascending directions were disappeared over all scales. Further analysis in specific bands showed that the reduced FCMC mainly focused on the alpha2 at higher scale, beta1 and beta2 across almost the entire scales. This study about multi-scale confirms that the FCMC between the brain and muscles is capable of complex and directional characteristics, and these characteristics in functional connection for stroke are destroyed by the structural lesion in the brain that might disrupt coordination, feedback, and information transmission in efferent control and afferent feedback. The study demonstrates for the first time the multiscale and directional characteristics of the FCMC for stroke patients, and provides a preliminary observation for application in clinical assessment following stroke.

Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity: Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses

Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder.

Comparing EEG Nonlinearity in Deficit and Nondeficit Schizophrenia Patients: Preliminary Data

Electroencephalogram (EEG) contains valuable information obtained noninvasively that can be used for assessment of brain's processing capacity of patients with psychiatric disorders. The purpose of the present work was to evaluate possible differences in EEG complexity between deficit (DS) and nondeficit (NDS) subtypes of schizophrenia as a reflection of the cognitive processing capacities in these groups. A particular nonlinear metric known as Lempel-Ziv complexity (LZC) was used as a computational tool in order to determine the randomness in EEG alpha band time series from 3 groups (deficit schizophrenia [n = 9], nondeficit schizophrenia [n = 10], and healthy controls [n = 10]) according to time series randomness. There was a significant difference in frontal EEG complexity between the DS and NDS subgroups ( p = .013), with DS group showing less complexity. A significant positive correlation was found between LZC values and Positive and Negative Syndrome Scale (PANSS) general psychopathology scores (ie, larger frontal EEG complexity correlated with more severe psychopathology), explained partially by the emotional component subscore of the PANSS. These findings suggest that cognitive processing occurring in the frontal networks in DS is less complex compared to NDS patients as reflected by EEG complexity measures. The data also suggest that there may be a relationship between the degree of emotionality and the complexity of the frontal EEG signal.

A Study of the Brain Functional Network of Post-Stroke Depression in Three Different Lesion Locations

Research on the mechanism of post stroke depression (PSD) is the key way to improve the treatment of PSD. However, the functional brain network of PSD has not been entirely supported by the results of functional magnetic resonance imaging (fMRI) studies. The aims of this study are to investigate the brain response of PSD in three different lesions. The brain responses of the three PSD subgroups were similar. However, each subgroup had its own characteristics of the brain network. In the temporal lobe subgroup, the right thalamus had increased degree centrality (DC) values which were different from the other two subgroups. In the frontal lobe subgroup, the left dorsolateral prefrontal cortex, caudate, and postcentral gyrus had increased DC values which were different from the other two subgroups. The hemodynamic response of PSD indicates that PSD has activities of similar emotional networks, of which the negative network realizes its function through the limbic system and default mode network. The brain network has unique characteristics for different lesion locations. The neurological function of the lesion location, the compensatory mechanism of the brain, and the mechanism of integrity and locality of the brain are the important factors in the individual emotional network.

A Study of the Brain Abnormalities of Post-Stroke Depression in Frontal Lobe Lesion

Post stroke depression (PSD) is a serious complication of stroke. Brain imaging is an important method of studying the mechanism of PSD. However, few studies have focused on the single lesion location. The aim of this study was to investigate the brain mechanism of frontal lobe PSD using combined voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI). In total, 30 first-time ischemic frontal lobe stroke patients underwent T1 weighted MRI and resting-state fMRI scans. Clinical assessments included the 24-item Hamilton Rating Scale for Depression, the National Institutes of Health Stroke Scale, and the Mini-Mental State Examination. In our result, decreased gray matter (GM) volume in patients was observed in the prefrontal cortex, limbic system and motor cortex. The anterior cingulate cortex, selected as a seed to perform connectivity analyses, showed a greatly decreased functional connectivity with the prefrontal cortex, cingulate cortex, and motor cortex, but had an increased functional connectivity with the hippocampus gyrus, parahippocampa gyrus, insular, and amygdala. Stroke lesion location reduces excitability of brain areas in the ipsilateral brain. PSD affects mood through the brain network of the prefrontal-limbic circuit. Some brain networks, including motor cortex and the default mode network, show other characteristics of PSD brain network.

A Novel Nonlinear Dynamic Method for Stroke Rehabilitation Effect Evaluation Using EEG

Evaluating the effect of stroke rehabilitation based on electroencephalogram (EEG) is still a challenging problem. This paper presents a novel nonlinear dynamic complexity method for the evaluation of stroke rehabilitation effect from EEG signal. Our method calculates the nonlinearly separable degree (NLSD) of EEG signal, and then employs an indicator, called mean nonlinearly separable complexity degree (Mean_NLSD), to efficiently and accurately evaluate therapy effect of stroke patients. This paper under twelve stimuli conditions on eleven patients and eleven control subjects indicates that in general Mean_NLSD is smaller at the lesion regions and that the Mean_NLSD of the control subjects is stochastic. Compared with conventional spectral methods, such as mean power spectral density (PSD), Mean_NLSD is more sensitive and robust. Overall Mean_NLSD may offer a promising approach to facilitate the evaluation of stroke rehabilitation effect.

Long-range temporal correlations of broadband EEG oscillations for depressed subjects following different hemispheric cerebral infarction

Abnormal long-range temporal correlation (LRTC) in EEG oscillation has been observed in several brain pathologies and mental disorders. This study examined the relationship between the LRTC of broadband EEG oscillation and depression following cerebral infarction with different hemispheric lesions to provide a novel insight into such depressive disorders. Resting EEGs of 16 channels in 18 depressed (9 left and 9 right lesions) and 21 non-depressed (11 left and 10 right lesions) subjects following cerebral infarction and 19 healthy control subjects were analysed by means of detrended fluctuation analysis, a quantitative measurement of LRTC. The difference among groups and the correlation between the severity of depression and LRTC in EEG oscillation were investigated by statistical analysis. The results showed that LRTC of broadband EEG oscillations in depressive subjects was still preserved but attenuated in right hemispheric lesion subjects especially in left pre-frontal and right inferior frontal and posterior temporal regions. Moreover, an association between the severity of psychiatric symptoms and the attenuation of the LRTC was found in frontal, central and temporal regions for stroke subjects with right lesions. A high discriminating ability of the LRTC in the frontal and central regions to distinguish depressive from non-depressive subjects suggested potential feasibility for LRTC as an assessment indicator for depression following right hemispheric cerebral infarction. Different performance of temporal correlation in depressed subjects following the two hemispheric lesions implied complex association between depression and stroke lesion location.

Altered cerebral blood flow velocity features in fibromyalgia patients in resting-state conditions

The aim of this study is to characterize in resting-state conditions the cerebral blood flow velocity (CBFV) signals of fibromyalgia patients. The anterior and middle cerebral arteries of both hemispheres from 15 women with fibromyalgia and 15 healthy women were monitored using Transcranial Doppler (TCD) during a 5-minute eyes-closed resting period. Several signal processing methods based on time, information theory, frequency and time-frequency analyses were used in order to extract different features to characterize the CBFV signals in the different vessels. Main results indicated that, in comparison with control subjects, fibromyalgia patients showed a higher complexity of the envelope CBFV and a different distribution of the power spectral density. In addition, it has been observed that complexity and spectral features show correlations with clinical pain parameters and emotional factors. The characterization features were used in a lineal model to discriminate between fibromyalgia patients and healthy controls, providing a high accuracy. These findings indicate that CBFV signals, specifically their complexity and spectral characteristics, contain information that may be relevant for the assessment of fibromyalgia patients in resting-state conditions.

Quantitative EEG abnormalities in major depressive disorder with basal ganglia stroke with lesions in different hemispheres

BACKGROUND

This study aimed to examine the aberrant EEG oscillation in major depressive subjects with basal ganglia stroke with lesions in different hemispheres.

METHODS

Resting EEG of 16 electrodes in 58 stroke subjects, 26 of whom had poststroke depression (13 with left-hemisphere lesion and 13 with right) and 32 of whom did not (18 with left lesion and 14 with right), was recorded to obtain spectral power analysis for several frequency bands. Multiple analysis of variance and receiver operating characteristic (ROC) curves were used to identify differences between poststroke depression (PSD) and poststroke non-depression (PSND), treating the different lesion hemispheres separately. Moreover, Pearson linear correlation analysis was conducted to test the severity of depressive symptoms and EEG indices.

RESULTS

PSD with left-hemisphere lesion showed increased beta2 power in frontal and central areas, but PSD with right-hemisphere lesion showed increased theta and alpha power mainly in occipital and temporal regions. Additionally, for left-hemisphere lesions, beta2 power in central and right parietal regions provided high discrimination between PSD and PSND, and for right-hemisphere lesions, theta power was similarly discriminative in most regions, especially temporal regions. We also explored the association between symptoms of depression and the power of abnormal bands, but we found no such relationship.

LIMITATIONS

The sample size was relatively small and included subjects with different lesions of the basal ganglia.

CONCLUSIONS

The aberrant EEG oscillation in subjects with PSD differs between subjects with lesions of the left and right hemispheres, suggesting a complex association between depression and lesion location in stroke patients.