Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression

Resting-state EEG spectral and fractal features in dementia with Lewy bodies with and without visual hallucinations

OBJECTIVE

Complex visual hallucinations (VH) are a core feature of dementia with Lewy bodies (DLB), though they may not occur in all patients. Power spectral density (PSD) analysis of resting-state EEG (rs-EEG) shows associations between some frequency bands (e.g., theta), individual alpha frequency (IAF) and VH. However, new tools that improve early differential diagnosis and symptom-based stratification with higher sensitivity and specificity, even within the DLB population, are desirable. We aimed to assess differences in rs-EEG data between DLB patients with VH (DLB-VH+) and without VH (DLB-VH-), comparing innovative non-linear approaches with more traditional linear ones.

METHODS

We retrospectively analyzed rs-EEG recordings of DLB-VH+, DLB-VH-, Alzheimer's disease patients and age-matched healthy controls. EEG was analyzed using the nonlinear Higuchi's Fractal Dimension (FD) measure, and the results were compared with those of entropy and standard linear methods based on PSD and IAF.

RESULTS

Only the FD measure could discriminate between DLB-VH+ and DLB-VH-.

CONCLUSIONS

In conclusion, rs-EEG differences between DLB-VH+ and DLB-VH- are better characterized by FD analysis than by a more traditional power spectrum approach.

SIGNIFICANCE

This suggests that the presence of complex VH is associated with less complex brain dynamics at rest, as reflected by the FD measure.

Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due to its non-invasive nature and cost-effectiveness. With the rise of computational psychiatry, the integration of EEG with artificial intelligence has yielded remarkable results in diagnosing depression. This review offers a comparative analysis of two predominant methodologies in research: traditional machine learning and deep learning methods. Furthermore, this review addresses key challenges in current research and suggests potential solutions. These insights aim to enhance diagnostic accuracy for depression and also foster further development in the area of computational psychiatry.

Dragon boat exercise reshapes the temporal-spatial dynamics of the brain

Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8-12 Hz) and β (13-30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale.

Immediate and long-term electrophysiological biomarkers of antidepressant-like behavioral effects after subanesthetic ketamine and medial prefrontal cortex deep brain stimulation treatment

Introduction

Both ketamine (KET) and medial prefrontal cortex (mPFC) deep brain stimulation (DBS) are emerging therapies for treatment-resistant depression, yet our understanding of their electrophysiological mechanisms and biomarkers is incomplete. This study investigates aperiodic and periodic spectral parameters, and the signal complexity measure sample entropy, within mPFC local field potentials (LFP) in a chronic corticosterone (CORT) depression model after ketamine and/or mPFC DBS.

Methods

Male rats were intraperitoneally administered CORT or vehicle for 21 days. Over the last 7 days, animals receiving CORT were treated with mPFC DBS, KET, both, or neither; then tested across an array of behavioral tasks for 9 days.

Results

We found that the depression-like behavioral and weight effects of CORT correlated with a decrease in aperiodic-adjusted theta power (5-10 Hz) and an increase in sample entropy during the administration phase, and an increase in theta peak frequency and a decrease in the aperiodic exponent once the depression-like phenotype had been induced. The remission-like behavioral effects of ketamine alone correlated with a post-treatment increase in the offset and exponent, and decrease in sample entropy, both immediately and up to eight days post-treatment. The remission-like behavioral effects of mPFC DBS alone correlated with an immediate decrease in sample entropy, an immediate and sustained increase in low gamma (20-50 Hz) peak width and aperiodic offset, and sustained improvements in cognitive function. Failure to fully induce remission-like behavior in the combinatorial treatment group correlated with a failure to suppress an increase in sample entropy immediately after treatment.

Conclusion

Our findings therefore support the potential of periodic theta parameters as biomarkers of depression-severity; and periodic low gamma parameters and cognitive measures as biomarkers of mPFC DBS treatment efficacy. They also support sample entropy and the aperiodic spectral parameters as potential cross-modal biomarkers of depression severity and the therapeutic efficacy of mPFC DBS and/or ketamine. Study of these biomarkers is important as objective measures of disease severity and predictive measures of therapeutic efficacy can be used to personalize care and promote the translatability of research across studies, modalities, and species.

Brain network entropy, depression, and quality of life in people with traumatic brain injury and seizure disorders

OBJECTIVE

Traumatic brain injury (TBI) often precedes the onset of epileptic (ES) or psychogenic nonepileptic seizures (PNES) with depression being a common comorbidity. The relationship between depression severity and quality of life (QOL) may be related to resting-state network complexity. We investigated these relationships in adults with TBI-only, TBI + ES, or TBI + PNES using Sample Entropy (SampEn), a measure of physiologic signals complexity.

METHODS

Adults with TBI-only (n = 60), TBI + ES (n = 21), or TBI + PNES (n = 56) completed the Beck Depression Inventory-II (BDI-II; depression symptom severity) and QOL in Epilepsy (QOLIE-31) assessments and underwent resting-state functional magnetic resonance imaging (rs-fMRI). SampEn values derived from six resting state functional networks were calculated per participant. Effects of group, network, and group-by-network-interactions for SampEn were investigated with a mixed-effects model. We examined relationships between BDI-II, QOL, and SampEn of each of the networks.

RESULTS

Groups did not differ in age, but there was a higher proportion of women with TBI + PNES (p = 0.040). TBI + ES and TBI-only groups did not differ in BDI-II or QOLIE-31 scores, while the TBI + PNES group scored worse on both measures. The fixed effects of the model revealed significant differences in SampEn values across networks (lower SampEn for the frontoparietal network compared to other networks). The likelihood ratio test for group-by-network-interactions was significant (p = 0.033). BDI-II was significantly negatively associated with Overall QOL scale scores in all groups, and significantly negatively associated with network SampEn values only in the TBI + PNES group.

SIGNIFICANCE

Only TBI + PNES had significant relationships between depression symptom severity and network SampEn values indicating that the resting state network complexity is related to depression severity in this group but not in TBI + ES or TBI-only.

PLAIN LANGUAGE SUMMARY

The brain has a complex network of internal connections. How well these connections work may be affected by TBI and seizures and may underlie mental health symptoms including depression; the worse the depression, the worse the quality of life. Our study compared brain organization in people with TBI, people with epilepsy after TBI, and people with nonepileptic seizures after TBI. Only people with nonepileptic seizures after TBI showed a relationship between how organized their brain connections were and how bad was their depression. We need to better understand these relationships to develop more impactful, effective treatments.

One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response

Introduction

To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response.

Methods

Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients.

Results

Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization.

Discussion

Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability.

Clinical trial registration

https://clinicaltrials.gov, identifier NCT00519428.

Changed brain entropy and functional connectivity patterns induced by electroconvulsive therapy in majoy depression disorder

OBJECTIVE

Our objective is to innovatively integrate both linear and nonlinear characteristics of brain signals in Electroconvulsive Therapy (ECT) research, with the goal of uncovering deeper insights into the pathogenesis of Major Depressive Disorder (MDD) and identifying novel targets for other physical intervention therapies.

METHODS

We measured brain entropy (BEN) in 42 MDD patients and 42 matched healthy controls (HC) using rs-fMRI data. Brain regions that differed significantly in patients with MDD before and after ECT were extracted. Then, we use these brain regions as seed points to investigate the differences in whole-brain resting-state functional connectivity (RSFC) patterns before and after ECT.

RESULTS

Compared to HCs, patients had higher BEN levels in the right precuneus (PCUN.R) and right angular gyrus (ANG.R). After ECT, patients had lower BEN levels in the PCUN.R and ANG.R. Compared with before ECT, patients showed significantly increased RSFC after ECT between the PCUN.R and right middle temporal gyrus and ANG.R. Significantly increased RSFC was observed between the ANG.R and right middle frontal gyrus and right supramarginal gyrus after ECT.

CONCLUSION

Combining the linear and nonlinear characteristics of brain signals can effectively explore the pathogenesis of depression and provide new targets for ECT.

Point of Care Testing (POCT) in Psychopathology Using Fractal Analysis and Hilbert Huang Transform of Electroencephalogram (EEG)

Research has shown that relying only on self-reports for diagnosing psychiatric disorders does not yield accurate results at all times. The advances of technology as well as artificial intelligence and other machine learning algorithms have allowed the introduction of point of care testing (POCT) including EEG characterization and correlations with possible psychopathology. Nonlinear methods of EEG analysis have significant advantages over linear methods. Empirical mode decomposition (EMD) is a reliable nonlinear method of EEG pre-processing. In this chapter, we compare two existing EEG complexity measures - Higuchi fractal dimension (HFD) and sample entropy (SE), with our newly proposed method using Higuchi fractal dimension from the Hilbert Huang transform (HFD-HHT). We present an example using the three complexity measures on a 2-minute EEG recorded from a healthy 20-year-old male after signal pre-processing. Furthermore, we showed the usefulness of these complexity measures in the classification of major depressive disorder (MDD) with healthy controls. Our study is in line with previous research and has shown an increase in HFD and SE values in the full, alpha and beta frequency bands suggestive of an increase in EEG irregularity. Moreover, the HFD-HHT values decreased in those three bands for majority of electrodes which is suggestive of a decrease in irregularity in the frequency-time domain. We conclude that all three complexity measures can be vital features useful for EEG analysis which could be incorporated in POCT systems.

Advances in Understanding Fractals in Affective and Anxiety Disorders

In this chapter, we review the research that has applied fractal measures to the study of the most common psychological disorders, that is, affective and anxiety disorders. Early studies focused on heart rate, but diverse measures have also been examined, from variations in subjective mood, or hand movements, to electroencephalogram or magnetoencephalogram data. In general, abnormal fractal dynamics in different physiological and behavioural outcomes have been observed in mental disorders. Despite the disparity of variables measured, fractal analysis has shown high sensitivity in discriminating patients from healthy controls. However, and because of this heterogeneity in measures, the results are not straightforward, and more studies are needed in this promising line.

Clinical Sensitivity of Fractal Neurodynamics

Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.

[Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention]

Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors' laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.

Immunometabolic alterations in older adults with heightened depressive symptom trajectories: a network approach

Objective: To analyse the patterns of relationships between depressive symptoms and immunometabolic markers across longitudinal depression status in older people. Methods: A sample of 3349 older adults (55.21% women; initial age: m = 58.44, sd = 5.21) from the English Longitudinal Study of Ageing was used. Participants were classified according to their longitudinal depression status: minimal depressive symptoms (n = 2736), depressive episode onset (n = 481), or chronic depression (n = 132). Network analysis was used to study the relationships between depression symptoms (CES-D 8 items), inflammatory (white blood cell, C-reactive protein, fibrinogen) and metabolic biomarkers (metabolic syndrome markers). Results: Network structure remained invariant across groups. The minimal symptom group had higher overall strength than both clinical groups (p < .01). Moreover, significant relationships between symptoms and markers were observed across group-specific networks. C-reactive protein and effort symptom were positively connected in the minimal symptom group but not in the other groups. Loneliness and diastolic blood pressure were positively associated only in the chronic depression group. Finally, metabolic markers were identified as central nodes in the clinical status networks. Conclusion: The network analysis constitutes a useful approach to disentangle pathophysiological relationships that may maintain mental disorders in old age.

A pattern of cognitive resource disruptions in childhood psychopathology

The Hurst exponent (H) isolated in fractal analyses of neuroimaging time series is implicated broadly in cognition. Within this literature, H is associated with multiple mental disorders, suggesting that H is transdimensionally associated with psychopathology. Here, we unify these results and demonstrate a pattern of decreased H with increased general psychopathology and attention-deficit/hyperactivity factor scores during a working memory task in 1,839 children. This pattern predicts current and future cognitive performance in children and some psychopathology in 703 adults. This pattern also defines psychological and functional axes associating psychopathology with an imbalance in resource allocation between fronto-parietal and sensorimotor regions, driven by reduced resource allocation to fronto-parietal regions. This suggests the hypothesis that impaired working memory function in psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand.

EEG alpha band characteristics in patients with a depressive episode within recurrent and bipolar depression

BACKGROUND

The search for biological markers for the differential diagnosis of recurrent depression and bipolar depression is an important undertaking in modern psychiatry. Electroencephalography (EEG) is one of the promising tools in addressing this challenge.

AIM

To identify differences in the quantitative characteristics of the electroencephalographic alpha band activity in patients with a depressive episode within the framework of recurrent depression and bipolar depression.

METHODS

Two groups of patients (all women) were formed: one consisting of subjects with recurrent depressive disorder and one with subjects experiencing a current mild/moderate episode (30 patients), and subjects with bipolar affective disorder or a current episode of mild or moderate depression (30 patients). The groups did not receive pharmacotherapy and did not differ in their socio-demographic parameters or total score on the Hamilton depression scale. A baseline electroencephalogram was recorded, and the quantitative characteristics of the alpha band activity were analyzed, including the absolute spectral power, interhemispheric coherence, and EEG activation.

RESULTS

The patients with recurrent depressive disorder demonstrated statistically significantly lower values of the average absolute spectral power of the alpha band (z=2.481; p=0.042), as well as less alpha attenuation from eyes closed to eyes open (z=2.573; p=0.035), as compared with the patients with bipolar affective disorder.

CONCLUSION

The presented quantitative characteristics of alpha activity are confirmation that patients with affective disorders of different origins also display distinctive electrophysiological features which can become promising biomarkers and could help separate bipolar depression from the recurrent type.

A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS) have been proven effective non-invasive treatments for patients with drug-resistant major depressive disorder (MDD). However, some depressed patients do not respond to these treatments. Therefore, the investigation of reliable and valid brain oscillations as potential indices for facilitating the precision of diagnosis and treatment protocols has become a critical issue. The current review focuses on brain oscillations that, mostly based on EEG power analysis and connectivity, distinguish between MDD and controls, responders and non-responders, and potential depression severity indices, prognostic indicators, and potential biomarkers for rTMS or iTBS treatment. The possible roles of each biomarker and the potential reasons for heterogeneous results are discussed, and the directions of future studies are proposed.

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population

Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms. However most of these studies have either explored resting state EEG (rs-EEG) data or task-based EEG data but not both, we seek to compare their respective efficacy. We work with data from non-clinically depressed individuals who score higher and lower on the depression scale and hence are more and less vulnerable to depression, respectively. Forty participants volunteered for the study. Questionnaires and EEG data were collected from participants. We found that people who are more vulnerable to depression had on average increased EEG amplitude in the left frontal channel, and decreased amplitude in the right frontal and occipital channels for raw data (rs-EEG). Task-based EEG data from a sustained attention to response task used to measure spontaneous thinking, an increased EEG amplitude in the central part of the brain for individuals with low vulnerability and an increased EEG amplitude in right temporal, occipital and parietal regions in individuals more vulnerable to depression were found. In an attempt to predict vulnerability (high/low) to depression, we found that a Long Short Term Memory model gave the maximum accuracy of 91.42% in delta wave for task-based data whereas 1D-Convolution neural network gave the maximum accuracy of 98.06% corresponding to raw rs-EEG data. Hence if one has to look at the primary question of which data will be good for predicting vulnerability to depression, rs-EEG seems to be better than task-based EEG data. However, if mechanisms driving depression like rumination or stickiness are to be understood, task-based data may be more effective. Furthermore, as there is no consensus as to which biomarker of rs-EEG is more effective in the detection of MDD, we also experimented with evolutionary algorithms to find the most informative subset of these biomarkers. Higuchi fractal dimension, phase lag index, correlation and coherence features were also found to be the most important features for predicting vulnerability to depression using rs-EEG. These findings bring up new possibilities for EEG-based machine/deep learning diagnostics in the future.

Evaluating EEG complexity metrics as biomarkers for depression

Nonlinear EEG analysis offers the potential for both increased diagnostic accuracy and deeper mechanistic understanding of psychopathology. EEG complexity measures have previously been shown to positively correlate with clinical depression. In this study, resting state EEG recordings were taken across multiple sessions and days with both eyes open and eyes closed conditions from a total of 306 subjects, 62 of which were in a current depressive episode, and 81 of which had a history of diagnosed depression but were not currently depressed. Three different EEG montages (mastoids, average, and Laplacian) were also computed. Higuchi fractal dimension (HFD) and sample entropy (SampEn) were calculated for each unique condition. The complexity metrics showed high internal consistency within session and high stability across days. Higher complexity was found in open-eye recordings compared to closed eyes. The predicted correlation between complexity and depression was not found. However, an unexpected sex effect was observed, in which males and females exhibited different topographic patterns of complexity.

When Heart Beats Differently in Depression: Review of Nonlinear Heart Rate Variability Measures

BACKGROUND

Disturbed heart dynamics in depression seriously increases mortality risk. Heart rate variability (HRV) is a rich source of information for studying this dynamics. This paper is a meta-analytic review with methodological commentary of the application of nonlinear analysis of HRV and its possibility to address cardiovascular diseases in depression.

OBJECTIVE

This paper aimed to appeal for the introduction of cardiological screening to patients with depression, because it is still far from established practice. The other (main) objective of the paper was to show that nonlinear methods in HRV analysis give better results than standard ones.

METHODS

We systematically searched on the web for papers on nonlinear analyses of HRV in depression, in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 framework recommendations. We scrutinized the chosen publications and performed random-effects meta-analysis, using the esci module in jamovi software where standardized effect sizes (ESs) are corrected to yield the proof of the practical utility of their results.

RESULTS

In all, 26 publications on the connection of nonlinear HRV measures and depression meeting our inclusion criteria were selected, examining a total of 1537 patients diagnosed with depression and 1041 healthy controls (N=2578). The overall ES (unbiased) was 1.03 (95% CI 0.703-1.35; diamond ratio 3.60). We performed 3 more meta-analytic comparisons, demonstrating the overall effectiveness of 3 groups of nonlinear analysis: detrended fluctuation analysis (overall ES 0.364, 95% CI 0.237-0.491), entropy-based measures (overall ES 1.05, 95% CI 0.572-1.52), and all other nonlinear measures (overall ES 0.702, 95% CI 0.422-0.982). The effectiveness of the applied methods of electrocardiogram analysis was compared and discussed in the light of detection and prevention of depression-related cardiovascular risk.

CONCLUSIONS

We compared the ESs of nonlinear and conventional time and spectral methods (found in the literature) and demonstrated that those of the former are larger, which recommends their use for the early screening of cardiovascular abnormalities in patients with depression to prevent possible deleterious events.

Directionally Sensitive Fractal Radiomics Compatible With Irregularly Shaped Magnetic Resonance Tumor Regions of Interest: Association With Osteosarcoma Chemoresistance

BACKGROUND

Computational analysis of routinely acquired MRI has potential to improve the tumor chemoresistance prediction and to provide decision support in precision medicine, which may extend patient survival. Most radiomic analytical methods are compatible only with rectangular regions of interest (ROIs) and irregular tumor shape is therefore an important limitation. Furthermore, the currently used analytical methods are not directionally sensitive.

PURPOSE

To implement a tumor analysis that is directionally sensitive and compatible with irregularly shaped ROIs.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 54 patients with histopathologic diagnosis of primary osteosarcoma on tubular long bones and with prechemotherapy MRI.

FIELD STRENGTH/SEQUENCE

A 1.5 T, T2-weighted-short-tau-inversion-recovery-fast-spin-echo.

ASSESSMENT

A model to explore associations with osteosarcoma chemo-responsiveness included MRI data obtained before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Osteosarcoma morphology was analyzed in the MRI data by calculation of the nondirectional two-dimensional (2D) and directional and nondirectional one-dimensional (1D) Higuchi dimensions (Dh). MAP chemotherapy response was assessed by histopathological necrosis.

STATISTICAL TESTS

The area under the receiver operating characteristic (ROC) curve (AUC) evaluated the association of the calculated features with the actual chemoresponsiveness, using tumor histopathological necrosis (95%) as the endpoint. Least absolute shrinkage and selection operator (LASSO) machine learning and multivariable regression were used for feature selection. Significance was set at <0.05.

RESULTS

The nondirectional 1D Dh reached an AUC of 0.88 in association with the 95% tumor necrosis, while the directional 1D analysis along 180 radial lines significantly improved this association according to the Hanley/McNeil test, reaching an AUC of 0.95. The model defined by variable selection using LASSO reached an AUC of 0.98. The directional analysis showed an optimal predictive range between 90° and 97° and revealed structural osteosarcoma anisotropy manifested by its directionally dependent textural properties.

DATA CONCLUSION

Directionally sensitive radiomics had superior predictive performance in comparison to the standard nondirectional image analysis algorithms with AUCs reaching 0.95 and full compatibility with irregularly shaped ROIs.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 1.

Clinical features and power spectral entropy of electroencephalography in Wilson's disease with dystonia

OBJECTIVE

To study the clinical features and power spectral entropy (PSE) of electroencephalography signals in Wilson's disease (WD) patients with dystonia.

METHODS

Several scale evaluations were performed to assess the clinical features of WD patients. Demographic information and electroencephalography signals were obtained in all subjects.

RESULTS

34 WD patients with dystonia were recruited in the case group and 24 patients without dystonia were recruited in the control group. 20 healthy individuals were included in the healthy control group. The mean body mass index (BMI) in the case group was significantly lower than that in the controls (p < .05). The case group had significantly higher SAS, SDS, and Bucco-Facial-Apraxia Assessment scores (p < .05). Total BADS scores in the case group were lower than those in the control group (p < .01). Note that 94.11% of the case group presented with dysarthria and 70.59% of them suffered from dysphagia. Dysphagia was mainly related to the oral preparatory stage and oral stage. Mean power spectral entropy (PSE) values in the case group were significantly different (p < .05) from those in the control group and the healthy group across the different tasks.

CONCLUSIONS

The patients with dystonia were usually accompanied with low BMI, anxiety, depression, apraxia, executive dysfunction, dysarthria and dysphagia. The cortical activities of the WD patients with dystonia seemed to be more chaotic during the eyes-closed and reading tasks but lower during the swallowing stages than those in the control group.

Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.

EEG microstate in first-episode drug-naive adolescents with depression

BACKGROUND

A growing number of studies have revealed significant abnormalities in EEG microstate in patients with depression, but these findings may be affected by medication. Therefore, how the EEG microstates abnormally change in patients with depression in the early stage and without the influence of medication has not been investigated so far.

METHODS

Resting-state EEG data and Hamilton Depression Rating Scale (HDRS) were collected from 34 first-episode drug-naïve adolescent with depression and 34 matched healthy controls. EEG microstate analysis was applied and nonlinear characteristics of EEG microstate sequences were studied by sample entropy and Lempel-Ziv complexity (LZC). The microstate temporal parameters and complexity were tried to train a SVM for classification of patients with depression.

RESULTS

Four typical EEG microstate topographies were obtained in both groups, but microstate C topography was significantly abnormal in depression patients. The duration of microstate B, C, D and the occurrence and coverage of microstate B significantly increased, the occurrence and coverage of microstate A, C reduced significantly in depression group. Sample entropy and LZC in the depression group were abnormally increased and were negatively correlated with HDRS. When the combination of EEG microstate temporal parameters and complexity of microstate sequence was used to classify patients with depression from healthy controls, a classification accuracy of 90.9% was obtained.

CONCLUSION

Abnormal EEG microstate has appeared in early depression, reflecting an underlying abnormality in configuring neural resources and transitions between distinct brain network states. EEG microstate can be used as a neurophysiological biomarker for early auxiliary diagnosis of depression.

The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection

Bipolar depression is treated wrongly as unipolar depression, on average, for 8 years. It is shown that this mismedication affects the occurrence of a manic episode and aggravates the overall condition of patients with bipolar depression. Significant effort was invested in early detection of depression and forecasting of responses to certain therapeutic approaches using a combination of features extracted from standard and online testing, wearables monitoring, and machine learning. In the case of unipolar depression, this approach yielded evidence that this data-based computational psychiatry approach would be helpful in clinical practice. Following a similar pipeline, we examined the usefulness of this approach to foresee a manic episode in bipolar depression, so that clinicians and family of the patient can help patient navigate through the time of crisis. Our projects combined the results from self-reported daily questionnaires, the data obtained from smart watches, and the data from regular reports from standard psychiatric interviews to feed various machine learning models to predict a crisis in bipolar depression. Contrary to satisfactory predictions in unipolar depression, we found that bipolar depression, having more complex dynamics, requires personalized approach. A previous work on physiological complexity (complex variability) suggests that an inclusion of electrophysiological data, properly quantified, might lead to better solutions, as shown in other projects of our group concerning unipolar depression. Here, we make a comparison of previously performed research in a methodological sense, revisiting and additionally interpreting our own results showing that the methodological approach to mania forecasting may be modified to provide an accurate prediction in bipolar depression.

Region-specific complexity of the intracranial EEG in the sleeping human brain

As the brain is a complex system with occurrence of self-similarity at different levels, a dedicated analysis of the complexity of brain signals is of interest to elucidate the functional role of various brain regions across the various stages of vigilance. We exploited intracranial electroencephalogram data from 38 cortical regions using the Higuchi fractal dimension (HFD) as measure to assess brain complexity, on a dataset of 1772 electrode locations. HFD values depended on sleep stage and topography. HFD increased with higher levels of vigilance, being highest during wakefulness in the frontal lobe. HFD did not change from wake to stage N2 in temporo-occipital regions. The transverse temporal gyrus was the only area in which the HFD did not differ between any two vigilance stages. Interestingly, HFD of wakefulness and stage R were different mainly in the precentral gyrus, possibly reflecting motor inhibition in stage R. The fusiform and parahippocampal gyri were the only areas showing no difference between wakefulness and N2. Stages R and N2 were similar only for the postcentral gyrus. Topographical analysis of brain complexity revealed that sleep stages are clearly differentiated in fronto-central brain regions, but that temporo-occipital regions sleep differently.

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules?

Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.

Comparison of Residual Depressive Symptoms, Functioning, and Quality of Life Between Patients with Recurrent Depression and First Episode Depression After Acute Treatment in China

OBJECTIVE

This prospective study aimed to investigate the prognosis and rehabilitation of patients with recurrent depression and first episode depression after acute treatment in China.

METHODS

A total of 434 patients with first-episode or recurrent depression who received acute treatment respectively from sixteen hospitals in thirteen cities in China were enrolled in this prospective study. All patients were followed up for 6 months after acute treatment. The following data were collected at baseline period and 1, 3, and 6 months after acute treatment: general information of patients, medication information and patient's condition changes, brief 16-item quick inventory of depressive symptomatology self-report (QIDS-SR16), patient health questionnaire-15 (PHQ-15), quality of life enjoyment and satisfaction questionnaire-short form (Q-LES-Q-SF), Sheehan disability scale (SDS) and digit symbol substitution test (DSST).

RESULTS

During the baseline period, there was a significant difference in QIDS-SR16 between recurrent patients and first-episode patients (p < 0.05), and there was no significant difference in other indicators (p > 0.05). At one month after acute treatment, there were significant differences in the total QIDS-SR16 score, the total Q-LES-SF score, the social life score, and the family life/home responsibilities score of SDS in patients with recurrent depression and first-episode depression (p < 0.05). At three months after acute treatment, there were significant differences in the total Q-LES-SF score and social life score of SDS in patients with recurrent depression and first-episode depression (p < 0.05). At six months after acute treatment, there were significant differences in the total QIDS-SR16 score, the social life score, and the total Q-LES-SF score in patients with recurrent depression and first-episode depression (p < 0.05). Compared with that data during the baseline period, the QIDS-SR16 scores and the SDS scores of all patients decreased, and the Q-LES-SF scores of all patients gradually increased as time went on during the consolidation period.

CONCLUSION

The recurrent patients have more severe social function impairment, depressive symptoms, and lower life quality than that of the first-episode depressed patients. Given the negative impact of depressed symptom on recurrent patient, more attention should be paid to the treatment of recurrent patient and recurrence prevention of first episode patient.

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.

Cognitive function: holarchy or holacracy?

Cognition is the most complex function of the brain. When exploring the inner workings of cognitive processes, it is crucial to understand the complexity of the brain's dynamics. This paper aims to describe the integrated framework of the cognitive function, seen as the result of organization and interactions between several systems and subsystems. We briefly describe several organizational concepts, spanning from the reductionist hierarchical approach, up to the more dynamic theory of open complex systems. The homeostatic regulation of the mechanisms responsible for cognitive processes is showcased as a dynamic interplay between several anticorrelated mechanisms, which can be found at every level of the brain's organization, from molecular and cellular level to large-scale networks (e.g., excitation-inhibition, long-term plasticity-long-term depression, synchronization-desynchronization, segregation-integration, order-chaos). We support the hypothesis that cognitive function is the consequence of multiple network interactions, integrating intricate relationships between several systems, in addition to neural circuits.

Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression

OBJECTIVES

Biomarkers of major depressive disorder (MDD), its phases and forms have long been sought. Objectives were to examine whether the complexity of EEG activity, measured by Higuchi's fractal dimension (HFD) and sample entropy (SampEn), differs between healthy subjects, patients in remission, and in episode phase of the recurrent depression and whether the changes are differentially distributed between hemispheres and cortical regions.

METHODS

Resting state EEG with eyes closed was recorded from 22 patients suffering from recurrent depression (11 in remission, 11 in the episode), and 20 age and sex-matched healthy control subjects. Artifact-free EEG epochs were analyzed by in-house developed programs running HFD and SampEn algorithms.

RESULTS

Depressed patients had higher HFD and SampEn complexity compared to healthy subjects. The complexity was higher in patients who were in remission than in those in the acute episode. Altered complexity was present in the frontal and centro-parietal regions when compared to control group. The complexity in frontal and parietal regions differed between the two phases of depressive disorder.

CONCLUSIONS

Complexity measures of EEG distinguish between the healthy controls, patients in remission and episode. Further studies are needed to establish whether these measures carry a potential to aid clinically relevant decisions about depression.