Dimensional complexity of the EEG in patients with posttraumatic stress disorder

qEEG Neuromarkers of Complex Childhood Trauma in Adolescents

Introduction. Complex childhood trauma (CCT) involves prolonged exposure to severe interpersonal stressors, leading to deficits in executive functioning and self-regulation during adolescence, a critical period for neurodevelopment. While qEEG parameters, particularly alpha oscillations, have been proposed as potential biomarkers for trauma, empirical documentation in developmental samples is limited. Aim. This preregistered study investigated whether adolescents with CCT exhibit qEEG patterns similar to those reported for PTSD, such as reduced posterior alpha power, increased individual alpha peak frequency (iAPF), right-lateralized alpha frequencies, and lower total EEG power (RMS) compared to controls. Materials and Methods. EEG data from 26 trauma-exposed adolescents and 28 controls, sourced from an open database, underwent similar preprocessing. qEEG features, including alpha power, iAPF, alpha asymmetry, and RMS, were extracted from eyes-open and eyes-closed conditions and analyzed using mixed ANOVAs. Results. Significant group differences were found in total EEG power, with trauma-exposed adolescents showing lower RMS than controls. No significant differences were found in posterior absolute alpha power, iAPF, or alpha asymmetry. However, we observed that posterior relative alpha power was higher in the trauma group, though the difference was not statistically significant but showing a small to medium effect size. Additionally, a negative correlation between CPTSD severity and EEG power in the EO condition was observed, suggesting trauma-related cortical hypoactivation. Conclusion. Reduced total EEG power and modified alpha dynamics may serve as candidate neuromarkers of CCT. These findings underscore the need for further research to validate qEEG biomarkers for understanding and diagnosing trauma-related disorders in developmental populations.

Prevalence of psychosocial interventions for pediatric dog bite injury: Is the bark actually worse than the bite?

Purpose

Long-term psychological effects may occur after childhood dog bite injuries. We performed a national survey to assess psychosocial interventions for children presenting with dog bite injuries to pediatric trauma centers.

Methods

A 26-question, online survey was administered to Pediatric Trauma Program Managers in the United States (n = 83). The survey queried whether institutions provide directed psychosocial interventions to pediatric dog bite injury patients in the Emergency Department, inpatient, or outpatient settings and the types of interventions being used. Descriptive statistics were performed to demonstrate survey results.

Results

In total, 28 American College of Surgeons or State-verified Pediatric Trauma Centers responded to the survey (n = 28/83, 34%). Of the respondents, 18 (64.3%) did not have any interventions in place to address the psychosocial effects of pediatric patients' dog bite injuries. Of the 10 (35.7%) institutions with interventions in place, the types of psychosocial resources offered included: automated order sets within the electronic medical record, specialized teams that assess the patient while hospitalized or outpatient, child psychology referrals initiated at discharge, pet therapy, and trauma resiliency programs.

Conclusion

Most institutions surveyed did not have protocols or interventions in place to address psychosocial disturbances in children with dog bite injuries. We provide the example of our institution's practice, in which automatic psychology consults are placed for every child who is admitted with a dog bite injury. Performing caregiver education in the emergency department, providing caregivers with regional psychosocial resources, and communicating with a child's pediatrician may promote the necessary standardized psychological screening and/or follow up of these patients.

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.

Two-channel EEG based diagnosis of panic disorder and major depressive disorder using machine learning and non-linear dynamical methods

The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.

Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review

The enigma of post-traumatic stress disorder (PTSD) is embedded in a complex array of physiological responses to stressful situations that result in disruptions in arousal and cognitions that characterise the psychological disorder. Deciphering these physiological patterns is complex, which has seen the use of machine learning (ML) grow in popularity. However, it is unclear to what extent ML has been used with physiological data, specifically, the electroencephalogram (EEG) and electrocardiogram (ECG) to further understand the physiological responses associated with PTSD. To better understand the use of EEG and ECG biomarkers, with and without ML, a scoping review was undertaken. A total of 124 papers based on adult samples were identified comprising 19 ML studies involving EEG and ECG. A further 21 studies using EEG data, and 84 studies employing ECG meeting all other criteria but not employing ML were included for comparison. Identified studies indicate classical ML methodologies currently dominate EEG and ECG biomarkers research, with derived biomarkers holding clinically relevant diagnostic implications for PTSD. Discussion of the emerging trends, algorithms used and their success is provided, along with areas for future research.

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.

Advancing PTSD Diagnosis and the Treatment of Trauma in Humanitarian Emergencies via Mobile Health: Protocol for a Proof-of-Concept Non-Randomized Controlled Trial

BACKGROUND

Decentralized health systems in Low and Middle-Income Countries (LMICs) impacted by humanitarian crises lack resources and a qualified workforce to attend to the overwhelming demand for mental health care in emergencies. Innovative approaches that are safe, cost-effective, and scalable are needed to address the burden of traumatic stress brought by emergencies. High mobile phone ownership rates combined with the precision of neural, cognitive, and biometric measures of trauma and its feasible integration with Artificial Intelligence (AI) makes digital application (app) interventions a promising pathway to promote precision diagnosis and high-impact care.

OBJECTIVE

The aims of this study are to advance methods for the objective diagnosis and treatment of trauma in emergencies across LMICs by examining (i) neural, cognitive, and biometric markers and (ii) the efficacy of the eResilience App, a neuroscience-informed mobile health mental health app intervention, via changes in clinical symptomatology, cognitive performance, and brain activity.

METHODS

Trauma-exposed African refugees residing in Australia were selected for this study. A research software version of the eResilience App with advanced monitoring capabilities was designed for the trial. Participants completed the eResilience App at home during a seven-day period. Clinical, cognitive, and electrophysiological data were collected during baseline and post-test to examine biomarkers of trauma and the efficacy of the proposed digital intervention for the treatment of trauma and its potential outcomes including depression, anxiety, physical symptoms, self-harm, substance misuse, and cognitive impairment. In addition, biofeedback, wellbeing, and subjective stress data points were collected via the app during the treatment week, followed by clinical interviews at 1, 3, 6 and 12-months post-intervention.

RESULTS

Data collection was conducted between 2018 and 2020. A total n=100 participants exposed to war were screened, n= 75 were enrolled and assigned to a trauma-exposed control (n=38) or Posttraumatic Stress Disorder (PTSD) condition (n=37), and n= 70 completed all baseline, treatment, and post-test assessments. A total n=62 of the n=70 who completed the intervention opted to enrol in the 3, 6 and 12-month follow-ups. Data collection is complete, and results are being prepared for publication. If proven efficacious, this proof-of-concept clinical trial will inform fully powered randomized clinical trials in LMICs to further develop AI-powered, app-based diagnostic and prognostic features, and determine the app's cross-cultural efficacy for the treatment of trauma in emergency settings.

CONCLUSIONS

This protocol provides researchers with a comprehensive background of the study rationale, a detailed guideline for replication studies interested in examining the feasibility and the efficacy of the eResilience App across varied demographics, and a robust framework for investigations of low-cost objective diagnostic markers in mental health interventions. Methodological limitations and suggestions are also provided.

CLINICALTRIAL

Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12616001205426. Universal Trial Number (UTN): U1111-1180-0347.

Neurofeedback self-regulating training in patients with Post traumatic stress disorder: A randomized controlled trial study protocol

Background

Post-traumatic stress disorder (PTSD) has become an important public health problem. However, the conventional therapeutic strategy, including pharmacotherapy and cognitive behavioral therapy, has limitations. Neurofeedback is a technique that utilizes electroencephalography (EEG) signaling to monitor human physiological functions and is widely used to treat patients with PTSD. The purpose of our study is to assess the efficacy and safety level of neurofeedback treatment in patients with PTSD using quantitative EEG.

Methods

This is a randomized, waitlist-controlled, assessor-blinded, clinical trial. Forty-six patients with PTSD will be randomly assigned at a 1:1 ratio into two groups. The participants in the treatment group will receive neurofeedback treatment for 50 min, twice a week, for 8 weeks (16 sessions). Quantitative EEG will be utilized to monitor the physiological functions and brain waves of the participants. A four-week follow-up period is planned. The participants in the control group will wait for 12 weeks. The primary outcome is the Korean version of PTSD Checklist-5 (PCL-5-K) score. The PCL-5-K scores on week 8 will be compared between the two groups. Anxiety, depression, insomnia, emotions, EEG, quality-of-life, and safety level will be assessed as secondary outcomes.

Discussion

This trial will describe a clinical research methodology for neurofeedback in patients with PTSD. The numerous subjective and objective secondary outcomes add to the value of this trial’s results. It will also suggest a therapeutic strategy for utilizing quantitative EEG in patients with PTSD. Our trial will provide basic evidence for the management of PTSD via an integrative treatment.

Trial registration

Clinical Research Information Service (CRIS): KCT0003271.

EEG for Diagnosis of Adult ADHD: A Systematic Review With Narrative Analysis

BACKGROUND

Attention deficit hyperactivity disorder is a common neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity and or impulsivity. Since the development of the concept, a reliable biomarker to aid diagnosis has been sought. One potential method is the use of electroencephalogram to measure neuronal activity. The aim of this review is to provide an up to date synthesis of the literature surrounding the potential use of electroencephalogram for diagnosis of attention deficit hyperactivity disorder in adulthood.

METHODS

A search of PsycINFO, PubMed, and EMBASE was undertaken in February 2019 for peer-reviewed articles exploring electroencephalogram patterns in adults (18 years with no upper limit) diagnosed with attention deficit hyperactivity disorder.

RESULTS

Differences in electroencephalogram activity are potentially unique to adult attention deficit hyperactivity disorder populations. Strongest support was derived for elevated levels of both absolute and relative theta power, alongside the observation that alpha activity is able to typically differentiate between adult attention deficit hyperactivity disorder and normative populations.

CONCLUSIONS

Electroencephalogram can have a use in clinical settings to aid adult attention deficit hyperactivity disorder diagnosis, but areas of inconsistency are apparent.

Linear and Nonlinear EEG-Based Functional Networks in Anxiety Disorders

Electrocortical network dynamics are integral to brain function. Linear and nonlinear connectivity applications enrich neurophysiological investigations into anxiety disorders. Discrete EEG-based connectivity networks are unfolding with some homogeneity for anxiety disorder subtypes. Attenuated delta/theta/beta connectivity networks, pertaining to anterior-posterior nodes, characterize panic disorder. Nonlinear measures suggest reduced connectivity of ACC as an executive neuro-regulator in germane "fear circuitry networks" might be more central than considered. Enhanced network complexity and theta network efficiency at rest define generalized anxiety disorder, with similar tonic hyperexcitability apparent in social anxiety disorder further extending to task-related/state functioning. Dysregulated alpha connectivity and integration of mPFC-ACC/mPFC-PCC relays implicated with attentional flexibility and choice execution/congruence neurocircuitry are observed in trait anxiety. Conversely, state anxiety appears to recruit converging delta and beta connectivity networks as panic, suggesting trait and state anxiety are modulated by discrete neurobiological mechanisms. Furthermore, EEG connectivity dynamics distinguish anxiety from depression, despite prevalent clinical comorbidity. Rethinking mechanisms implicated in the etiology, maintenance, and treatment of anxiety from the perspective of EEG network science across micro- and macroscales serves to shed light and move the field forward.

The Electrical Aftermath: Brain Signals of Posttraumatic Stress Disorder Filtered Through a Clinical Lens

This review aims to identify patterns of electrical signals identified using electroencephalography (EEG) linked to posttraumatic stress disorder (PTSD) diagnosis and symptom dimensions. We filter EEG findings through a clinical lens, evaluating nuances in findings according to study criteria and participant characteristics. Within the EEG frequency domain, greater right than left parietal asymmetry in alpha band power is the most promising marker of PTSD symptoms and is linked to exaggerated physiological arousal that may impair filtering of environmental distractors. The most consistent findings within the EEG time domain focused on event related potentials (ERPs) include: 1) exaggerated frontocentral responses (contingent negative variation, mismatch negativity, and P3a amplitudes) to task-irrelevant distractors, and 2) attenuated parietal responses (P3b amplitudes) to task-relevant target stimuli. These findings suggest that some individuals with PTSD suffer from attention dysregulation, which could contribute to problems concentrating on daily tasks and goals in lieu of threatening distractors. Future research investigating the utility of alpha asymmetry and frontoparietal ERPs as diagnostic and predictive biomarkers or intervention targets are recommended.

Multiscale permutation Rényi entropy and its application for EEG signals

There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. However, some traditional complexity measure algorithms only quantify the complexities of signals, but cannot discriminate different signals very well. To analyze the complexity of epileptic EEG signals better, a new multiscale permutation Rényi entropy (MPEr) algorithm is proposed. In this algorithm, the coarse-grained procedure is introduced by using weighting-averaging method, and the weighted factors are determined by analyzing nonlinear signals. We apply the new algorithm to analyze epileptic EEG signals. The experimental results show that MPEr algorithm has good performance for discriminating different EEG signals. Compared with permutation Rényi entropy (PEr) and multiscale permutation entropy (MPE), MPEr distinguishes different EEG signals successfully. The proposed MPEr algorithm is effective and has good applications prospects in EEG signals analysis.

Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls

We employ a time-dependent Hurst analysis to identify EEG signals that differentiate between healthy controls and combat-related PTSD subjects. The Hurst exponents, calculated using a rescaled range analysis, demonstrate a significant differential response between healthy and PTSD samples which may lead to diagnostic applications. To overcome the non-stationarity of EEG data, we apply an appropriate window length wherein the EEG data displays stationary behavior. We then use the Hurst exponents for each channel as hypothesis test statistics to identify differences between PTSD cases and controls. Our study included a cohort of 12 subjects with half healthy controls. The Hurst exponent of the PTSD subjects is found to be significantly smaller than the healthy controls in channel F3. Our results indicate that F3 may be a useful channel for diagnostic applications of Hurst exponents in distinguishing PTSD and healthy subjects.

EEG in psychiatric practice: to do or not to do?

Electroencephalography (EEG) is a non-invasive investigation that can aid the diagnosis of psychiatric and neuropsychiatric disorders. A good predictor of an abnormal EEG recording is the presence of an organic factor identified during the clinical assessment. The non-invasiveness and low cost of the procedure and its ability to measure spontaneous brain activity appear to attract clinicians to utilise this investigative tool. However, studies have reported that EEGs arising from psychiatric referrals have the lowest abnormality detection rate. The focus of this article is to improve this by highlighting the current pitfalls and providing recommendations for appropriate utilisation of EEG. We describe specific EEG changes associated with major psychiatric disorders. We conclude by offering pragmatic considerations when referring a patient for EEG, emphasising the fact that the information provided to the neurophysiologist plays a crucial role in interpreting the EEG recording in a diagnostically meaningful way.

Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG

Humans are high-dimensional, complex systems consisting of many components that must coordinate in order to perform even the simplest of activities. Many behavioral studies, especially in the movement sciences, have advanced the notion of soft-assembly to describe how systems with many components coordinate to perform specific functions while also exhibiting the potential to re-structure and then perform other functions as task demands change. Consistent with this notion, within cognitive neuroscience it is increasingly accepted that the brain flexibly coordinates the networks needed to cope with changing task demands. However, evaluation of various indices of soft-assembly has so far been absent from neurophysiological research. To begin addressing this gap, we investigated task-related changes in two distinct indices of soft-assembly using the established phenomenon of EEG repetition suppression. In a repetition priming task, we assessed evidence for changes in the correlation dimension and fractal scaling exponents during stimulus-locked event-related potentials, as a function of stimulus onset and familiarity, and relative to spontaneous non-task-related activity. Consistent with predictions derived from soft-assembly, results indicated decreases in dimensionality and increases in fractal scaling exponents from resting to pre-stimulus states and following stimulus onset. However, contrary to predictions, familiarity tended to increase dimensionality estimates. Overall, the findings support the view from soft-assembly that neural dynamics should become increasingly ordered as external task demands increase, and support the broader application of soft-assembly logic in understanding human behavior and electrophysiology.

Complexity and nonlinear biomarkers in emotional disorders: A meta-analytic study

This meta-analysis aimed at gathering and summarising the findings on nonlinear biomarkers in the field of emotional disorders under the hypothesis that diseased systems show lowered complexity and hence less flexibility to adjust daily contexts. Scientific manuscripts from 1970 to 2014 were reviewed, 58 articles were analysed, and independent meta-analyses on anxiety disorders, bipolar disorders, and depressive disorders were conducted. Results revealed that anxious patients exhibited lower complexity than controls (p<0.05) despite panic patients showed more irregular respiratory activity. Inconclusive results were found for bipolar patients but pointed to higher randomness when suffering manic episodes. Finally, depressed patients showed a loss of complexity in the cardiac system and a loss of orderliness (despite a higher complexity) in brain and stress-related hormonal systems. As a conclusion, our findings highlight that either a loss of complexity or a loss of ordered complexity characterise the physiological systems of patients with emotional disorders. Several considerations for complexity, its related measurements, and suggestions for further research are discussed.

Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task

OBJECTIVE

The aim of this study was to investigate electroencephalogram (EEG) dynamics using complexity analysis in children with attention-deficit/hyperactivity disorder (ADHD) compared with healthy control children when performing a cognitive task.

METHOD

Thirty 7-12-year-old children meeting Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) criteria for ADHD and 30 healthy control children underwent an EEG evaluation during a cognitive task, and Lempel-Ziv complexity (LZC) values were computed. There were no significant differences between ADHD and control groups on age and gender.

RESULTS

The mean LZC of the ADHD children was significantly larger than healthy children over the right anterior and right posterior regions during the cognitive performance. In the ADHD group, complexity of the right hemisphere was higher than that of the left hemisphere, but the complexity of the left hemisphere was higher than that of the right hemisphere in the normal group.

CONCLUSION

Although fronto-striatal dysfunction is considered conclusive evidence for the pathophysiology of ADHD, our arithmetic mental task has provided evidence of structural and functional changes in the posterior regions and probably cerebellum in ADHD.

EEG correlates of the severity of posttraumatic stress symptoms: A systematic review of the dimensional PTSD literature

BACKGROUND

Considering the Research Domain Criteria (RDoC) framework, it is crucial to investigate posttraumatic stress disorder (PTSD) as a spectrum that ranges from normal to pathological. This dimensional approach is especially important to aid early PTSD detection and to guide better treatment options. In recent years, electroencephalography (EEG) has been used to investigate PTSD; however, reviews regarding EEG data related to PTSD are lacking, especially considering the dimensional approach. This systematic review examined the literature regarding EEG alterations in trauma-exposed people with posttraumatic stress symptoms (PTSS) to identify putative EEG biomarkers of PTSS severity.

METHOD

A systematic review of EEG studies of trauma-exposed participants with PTSS that reported dimensional analyses (e.g., correlations or regressions) between PTSS and EEG measures was performed.

RESULTS

The literature search yielded 1178 references, of which 34 studies were eligible for inclusion. Despite variability among the reviewed studies, the PTSS severity was often associated with P2, P3-family event-related potentials (ERPs) and alpha rhythms.

LIMITATIONS

The search was limited to articles published in English; no information about non-published studies or studies reported in other languages was obtained. Another limitation was the heterogeneity of studies, which made meta-analysis challenging.

CONCLUSIONS

EEG provides promising candidates to act as biomarkers, although further studies are required to confirm the findings. Thus, EEG, in addition to being cheaper and easier to implement than other central techniques, has the potential to reveal biomarkers of PTSS severity.

Complexity of spontaneous brain activity in mental disorders

Recent reports of functional and anatomical studies have provided evidence that aberrant neural connectivity lies at the heart of many mental disorders. Information related to neural networks has elucidated the nonlinear dynamical complexity in brain signals over a range of temporal scales. The recent advent of nonlinear analytic methods, which have served for the quantitative description of the brain signal complexity, has provided new insights into aberrant neural connectivity in many mental disorders. Although many studies have underpinned aberrant neural connectivity, findings related to complexity behavior are still inconsistent. This inconsistency might result from (i) heterogeneity in mental disorders, (ii) analytical issues, (iii) interference of typical development and aging. First, most mental disorders are heterogeneous in their clinical feature or intrinsic pathological mechanisms. Second, neurophysiologic output signals from complex brain connectivity might be characterized with multiple time scales or frequencies. Finally, age-related brain complexity changes must be considered when investigating pathological brain because typical brain complexity is not constant across generations. Future systematic studies addressing these issues will greatly expand our knowledge of neural connections and dynamics related to mental disorders.

Is mental illness complex? From behavior to brain

A defining but elusive feature of the human brain is its astonishing complexity. This complexity arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various amazing mental functions. In mentally ill patients, such adaptability is often impaired, leading to either ordered or random patterns of behavior. Quantification and classification of these abnormal human behaviors exhibited during mental illness is one of the major challenges of contemporary psychiatric medicine. In the past few decades, attempts have been made to apply concepts adopted from complexity science to better understand complex human behavior. Although considerable effort has been devoted to studying the abnormal dynamic processes involved in mental illness, unfortunately, the primary features of complexity science are typically presented in a form suitable for mathematicians, physicists, and engineers; thus, they are difficult for practicing psychiatrists or neuroscientists to comprehend. Therefore, this paper introduces recent applications of methods derived from complexity science for examining mental illness. We propose that mental illness is loss of brain complexity and the complexity of mental illness can be studied under a general framework by quantifying the order and randomness of dynamic macroscopic human behavior and microscopic neuronal activity. Additionally, substantial effort is required to identify the link between macroscopic behaviors and microscopic changes in the neuronal dynamics within the brain.

HIRREM™: a noninvasive, allostatic methodology for relaxation and auto-calibration of neural oscillations

Disturbances of neural oscillation patterns have been reported with many disease states. We introduce methodology for HIRREM™ (high-resolution, relational, resonance-based electroencephalic mirroring), also known as Brainwave Optimization™, a noninvasive technology to facilitate relaxation and auto-calibration of neural oscillations. HIRREM is a precision-guided technology for allostatic therapeutics, intended to help the brain calibrate its own functional set points to optimize fitness. HIRREM technology collects electroencephalic data through two-channel recordings and delivers a series of audible musical tones in near real time. Choices of tone pitch and timing are made by mathematical algorithms, principally informed by the dominant frequency in successive instants of time, to permit resonance between neural oscillatory frequencies and the musical tones. Relaxation of neural oscillations through HIRREM appears to permit auto-calibration toward greater hemispheric symmetry and more optimized proportionation of regional spectral power. To illustrate an application of HIRREM, we present data from a randomized clinical trial of HIRREM as an intervention for insomnia (n = 19). On average, there was reduction of right-dominant temporal lobe high-frequency (23-36 Hz) EEG asymmetry over the course of eight successive HIRREM sessions. There was a trend for correlation between reduction of right temporal lobe dominance and magnitude of insomnia symptom reduction. Disturbances of neural oscillation have implications for both neuropsychiatric health and downstream peripheral (somatic) physiology. The possibility of noninvasive optimization for neural oscillatory set points through HIRREM suggests potentially multitudinous roles for this technology. Research is currently ongoing to further explore its potential applications and mechanisms of action.

NONLINEAR ANALYSIS OF PHYSIOLOGICAL SIGNALS: A REVIEW

This paper reviews various nonlinear analysis methods for physiological signals. The assessment is based on a discussion of chaos-inspired methods, such as fractal dimension (FD), correlation dimension (D2), largest Lyapunov exponet (LLE), Renyi's entropy (REN), Shannon spectral entropy (SEN), and approximate entropy (ApEn). We document that these methods are used to extract discriminative features from electroencephalograph (EEG) and heart rate variability (HRV) signals by reviewing the relevant scientific literature. EEG features can be used to support the diagnosis of epilepsy and HRV features can be used to support the diagnosis of cardiovascular diseases as well as diabetes. Documenting the widespread use of these and other nonlinear methods supports our thesis that the study of feature extraction methods, based on the chaos theory, is an important subject which has been gaining more and significance in biomedical engineering. We adopt the position that pursuing research in the field of biomedical engineering is ultimately a progmatic activity, where it is necessary to engage in features that work. In this case, the nonlinear features are working well, even if we do not have conclusive evidence that the underlying physiological phenomena are indeed chaotic.

Hemispheric asymmetry in non-linear interdependence of EEG in post-traumatic stress disorder

AIM

While volumetric and metabolic imaging on post-traumatic stress disorder (PTSD) patients has been intensively performed, few studies using electroencephalograms (EEG) have been done as yet. The aim of the present study was to investigate abnormalities in functional connectivity of cortical networks in PTSD.

METHODS

Non-linear interdependence (NI), a measure of bidirectional, non-linear information transmission between two time series, was used. Resting EEG were recorded for 18 PTSD patients and 18 sex-matched healthy subjects on 16 channels with their eyes closed.

RESULTS

The NI patterns in PTSD patients were hemisphere asymmetric: an increase in NI in the fronto-parieto-temporal regions of the left hemisphere (F7, F3, T3, C3, T5 and P3) and a decrease in the fronto-parieto-occipital regions of the right hemisphere (F4, C4, P4 and O2). The non-linearity of NI in EEG, estimated from the surrogate data method, exhibited an increase in the PTSD patients as compared with that of healthy subjects, particularly in the left hemispheric cortex.

CONCLUSION

Abnormal functional connectivity in PTSD can be assessed using NI, a measure of multi-channel EEG.

Evidence-based medicine evaluation of electrophysiological studies of the anxiety disorders

We provide a systematic, evidence-based medicine (EBM) review of the field of electrophysiology in the anxiety disorders. Presently, electrophysiological studies of anxiety focus primarily on etiological aspects of brain dysfunction. The review highlights many functional similarities across studies, but also identifies patterns that clearly differentiate disorder classifications. Such measures offer clinical utility as reliable and objective indicators of brain dysfunction in individuals and indicate potential as biomarkers for the improvement of diagnostic specificity and for informing treatment decisions and prognostic assessments. Common to most of the anxiety disorders is basal instability in cortical arousal, as reflected in measures of quantitative electroencephalography (qEEG). Resting electroencephalographic (EEG) measures tend to correlate with symptom sub-patterns and be exacerbated by condition-specific stimulation. Also common to most of the anxiety disorders are condition-specific difficulties with sensory gating and the allocation and deployment of attention. These are clearly evident from evoked potential (EP) and event-related potential (ERP) electrical measures of information processing in obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), panic disorder (PD), generalized anxiety disorder (GAD) and the phobias. Other'ERP measures clearly differentiate the disorders. However, there is considerable variation across studies, with inclusion and exclusion criteria, medication status and control group selection not standardized within condition or across studies. Study numbers generally preclude analysis for confound removal or for the derivation of diagnostic biomarker patterns at this time. The current trend towards development of databases of brain and cognitive function is likely to obviate these difficulties. In particular, electrophysiological measures of function are likely to play a significant role in the development and subsequent adaptations of DSM-V and assist critically in securing improvements in nosological and treatment specificity.

Linear versus non-linear measures of temporal variability in finger tapping and their relation to performance on open- versus closed-loop motor tasks: comparing standard deviations to Lyapunov exponents

The nature of temporal variability during speeded finger tapping was examined using linear (standard deviation) and non-linear (Lyapunov exponent) measures. Experiment 1 found that right hand tapping was characterised by lower amounts of both linear and non-linear measures of variability than left hand tapping, and that linear and non-linear measures of variability were often negatively correlated with one another. Experiment 2 found that increased non-linear variability was associated with relatively enhanced performance on a closed-loop motor task (mirror tracing) and relatively impaired performance on an open-loop motor task (pointing in a dark room), especially for left hand performance. The potential uses and significance of measures of non-linear variability are discussed.

The impact of unintentional pediatric trauma: a review of pain, acute stress, and posttraumatic stress

This article reviews current research on acute stress disorder (ASD) and posttraumatic stress disorder (PTSD) resulting from pediatric simple (i.e., single, unpredictable, and unintentional) physical injury and how pain may act as both a trigger and a coexisting symptom. Although several studies have explored predictors of ASD and PTSD, as well as the relationship between these conditions in adults, there is less research on ASD and PTSD in children and adolescents. This review highlights the importance of early detection of pain and acute stress symptoms resulting from pediatric unintentional physical injury in the hopes of preventing long-term negative outcomes, such as the potential development of PTSD and associated academic, social, and psychological problems.

Genetic influences on dynamic complexity of brain oscillations

Human electroencephalogram (EEG) consists of complex aperiodic oscillations that are assumed to indicate underlying neural dynamics such as the number and degree of independence of oscillating neuronal networks. EEG complexity can be estimated using measures derived from nonlinear dynamic systems theory. Variations in such measures have been shown to be associated with normal individual differences in cognition and some neuropsychiatric disorders. Despite the increasing use of EEG complexity measures for the study of normal and abnormal brain functioning, little is known about genetic and environmental influences on these measures. Using the pointwise dimension (PD2) algorithm, this study assessed heritability of EEG complexity at rest in a sample of 214 young female twins consisting of 51 monozygotic (MZ) and 56 dizygotic (DZ) pairs. In MZ twins, intrapair correlations were high and statistically significant; in DZ twins, correlations were substantially smaller. Genetic analyses using linear structural equation modeling revealed high and significant heritability of EEG complexity: 62-68% in the eyes-closed condition, and 46-60% in the eyes-open condition. Results suggest that individual differences in the complexity of resting electrocortical dynamics are largely determined by genetic factors. Neurophysiological mechanisms mediating genetic variation in EEG complexity may include the degree of structural connectivity and functional differentiation among cortical neuronal assemblies.

Alterations in cerebral perfusion in posttraumatic stress disorder patients without re-exposure to accident-related stimuli

Functional neuroimaging studies have shown abnormalities of limbic regions in patients with posttraumatic stress disorder (PTSD) during symptom provocation and cognitive activation.

OBJECTIVE

The aim of this study was to determine whether PTSD patients without re-exposure to accident-related stimuli would exhibit alterations in cerebral perfusion compared with age-matched normal subjects.

METHODS

Brain perfusion SPECT was measured in medication-free 23 PTSD patients and 64 age-matched healthy subjects under resting conditions and analyzed using statistical parametric mapping to compare between the patient and control groups.

RESULTS

We found that PTSD patients exhibited increased cerebral blood perfusion in limbic regions and decreased perfusion in the superior frontal gyrus and parietal and temporal regions in comparison with those of the normal controls.

CONCLUSIONS

This result indicates that PTSD patients have alterations in cerebral perfusion of limbic regions and the frontal and temporal cortex without re-exposure to accident-related stimuli.

SIGNIFICANCE

This finding supports the hypothesis of the involvement of limbic regions, which might be associated with the regulation of emotion and memory, in the pathophysiology of PTSD.

Abnormal frontal and parietal activity during working memory updating in post-traumatic stress disorder

This study used event-related potentials (ERPs) to investigate the timing and scalp topography of working memory in post-traumatic stress disorder (PTSD). This study was designed to investigate ERPs associated with a specific working memory updating process. ERPs were recorded from 10 patients and 10 controls during two visual tasks where (a) targets were a specific word or (b) targets were consecutive matching words. In the first task, nontarget words are not retained in working memory. In the second task, as in delay-match-to-sample tasks, a non-target word defines a new target identity, so these words are retained in working memory. This working memory updating process was related to large positive ERPs over frontal and parietal areas at 400-800 ms, which were smaller in PTSD. Estimation of cortical source activity indicated abnormal patterns of frontal and parietal activity in PTSD, which were also observed in regional cerebral blood flow [Clark, C.R., McFarlane, A.C., Morris, P., Weber, D.L., Sonkkilla, C., Shaw, M., Marcina, J., Tochon-Danguy, H., Egan, G., 2003. Cerebral function in posttraumatic stress disorder during verbal working memory updating: a positron emission tomography study. Biological Psychiatry 53, 474-481]. Frontal and parietal cortex are known to be involved in distributed networks for working memory processes, interacting with medial temporal areas during episodic memory processes. Abnormal function in these brain networks helps to explain everyday concentration and memory difficulties in PTSD.

Central and peripheral psychophysiological responses to trauma-related cues in subclinical posttraumatic stress disorder: a pilot study

This study examined verbal-subjective, peripheral and central physiological responses of motor vehicle accident (MVA) survivors with subclinical posttraumatic stress disorder (PTSD), without PTSD symptoms as well as healthy controls. Seven persons of each group were exposed to positive, neutral, accident-related and negative, non-accident-related slides. The verbal-subjective ratings of the slides did not differ between the groups. In contrast to the verbal ratings of the trauma-related materials, the behavioral and physiological responses showed a remarkable dissociation from these reports. The startle responses were enhanced to accident-related slides only in the PTSD group and MVA survivors with PTSD had a significantly lower response to the neutral slides than MVA survivors without PTSD. P200 was lower to positive, neutral and negative slides in the PTSD group compared to both other groups. The late positive complex showed no group-related effects. The data suggest that traumatized persons with PTSD show exaggerated emotional responses to trauma-related stimuli and reduced cognitive responses to several types of stimuli that may interfere with the extinction of the emotional trauma memory.