Depression as a dynamical disease

The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder

We study how obsessive-compulsive disorder (OCD) affects the complexity and time-reversal symmetry-breaking (irreversibility) of the brain resting-state activity as measured by magnetoencephalography (MEG). Comparing MEG recordings from OCD patients and age/sex matched control subjects, we find that irreversibility is more concentrated at faster time scales and more uniformly distributed across different channels of the same hemisphere in OCD patients than in control subjects. Furthermore, the interhemispheric asymmetry between homologous areas of OCD patients and controls is also markedly different. Some of these differences were reduced by 1-year of Kundalini Yoga meditation treatment. Taken together, these results suggest that OCD alters the dynamic attractor of the brain's resting state and hint at a possible novel neurophysiological characterization of this psychiatric disorder and how this therapy can possibly modulate brain function.

Changes in Electrical Brain Activity and Cognitive Functions Following Mild to Moderate COVID-19: A one-Year Prospective Study After Acute Infection

The coronavirus disease 2019 (COVID-19) can disrupt various brain functions. Over a one-year period, we aimed to assess brain activity and cognitive function in 53 COVID-19 patients and 30 individuals without COVID-19 (or asymptomatic). The Montreal Cognitive Assessment, Trail Making Test Parts A and B (TMT-A and B), and Digit Span Test were used to assess cognitive function. Cognitive variables and electroencephalography (EEG) data (activity, mobility, and complexity) were compared between the groups at rest and during cognitive demand (F3-F7, Fz-F3, Fz-F4, and F4-F8). There was a reduction in F3-F7 activity during the TMT-B in the COVID-19 group at 6-12 months compared to the controls (p = 0.01) at baseline (p = 0.03), a reduction in signal complexity at F3-F7 at rest in the COVID-19 group at baseline and 6-12 months compared to the controls (p < 0.001), and a reduction in Fz-F4 activity at rest from 6-12 months in the post-COVID group compared to baseline (p = 0.02) and 3-6 months (p = 0.04). At 6-12 months, there was a time increase in TMT-A in the COVID-19 group compared to that in the controls (p = 0.04). Some correlations were found between EEG data and cognitive test in both groups. In conclusion, there was a reduction in brain activity at rest in the Fz-F4 areas and during high cognitive demands in the F3-F7 areas. A reduction in signal complexity in F3-F7 at rest was found in the COVID-19 group at 6-12 months after acute infection. Furthermore, individuals with COVID-19 experience long-term changes in cognitive function.

Contrasting Electroencephalography-Derived Entropy and Neural Oscillations With Highly Skilled Meditators

Meditation is an umbrella term for a number of mental training practices designed to improve the monitoring and regulation of attention and emotion. Some forms of meditation are now being used for clinical intervention. To accompany the increased clinical interest in meditation, research investigating the neural basis of these practices is needed. A central hypothesis of contemplative neuroscience is that meditative states, which are unique on a phenomenological level, differ on a neurophysiological level. To identify the electrophysiological correlates of meditation practice, the electrical brain activity of highly skilled meditators engaging in one of six meditation styles (shamatha, vipassana, zazen, dzogchen, tonglen, and visualization) was recorded. A mind-wandering task served as a control. Lempel-Ziv complexity showed differences in nonlinear brain dynamics (entropy) during meditation compared with mind wandering, suggesting that meditation, regardless of practice, affects neural complexity. In contrast, there were no differences in power spectra at six different frequency bands, likely due to the fact that participants engaged in different meditation practices. Finally, exploratory analyses suggest neurological differences among meditation practices. These findings highlight the importance of studying the electroencephalography (EEG) correlates of different meditative practices.

Lack of a Sense of Threat and Higher Emotional Lability in Patients With Chronic Microvascular Ischemia as Measured by Non-linear EEG Parameters

We aimed to investigate the non-linear features of the electroencephalogram (EEG) findings in patients with chronic microvascular ischemia (CMI) in order to determine the brain correlates of emotional impairment that could impact the risk of developing acute ischemia. We compared the EEG responses of patients with CMI and age-matched healthy volunteers to non-verbal emotionally charged sounds. We analyzed the EEG data, the psychological assessment of the stimuli, and the results of neuropsychological and behavioral testing. We assessed the (in)stability of the envelope's amplitude by calculating its average frequency and the ratio of its standard deviation to its mean. The non-linear features were lower in the patient group in the resting state. The emotional stimulation induced a decrease in the frequency of the envelope's amplitude in all subjects. Changes in the fractal dimension during stimulation were only seen in the patient group, and they correlated with symptoms of emotional lability. The lower ratio of the alpha-rhythm envelope's standard deviation to its mean in the right hemisphere correlated with a higher sense of threat. The EEG and behavioral correlates of emotional impairment in patients with CMI were found.

Nonlinear EEG parameters of emotional perception in patients with moderate traumatic brain injury, coma, stroke and schizophrenia

Objective

The aim of this study was to determine the EEG changes induced by emotional non-verbal sounds using nonlinear signals' features and also to examine the subjective emotional response in patients with different neurological and psychiatric disorders.

Methods

141 subjects participated in our study: patients after moderate TBI, patients in acute coma, patients after stroke, patients with schizophrenia and controls. 7 types of emotionally charged stimuli were presented. Non-comatose participants were asked to assess the levels of experienced emotions. We analyzed fractal dimension, signal's envelope parameters and Hjorth mobility and complexity.

Results

The Hjorth parameters were negatively correlated with irritation. The fractal dimension was positively correlated with arousal and empathy levels. The only presentation of laughter to post-stroke patients induced the reaction similar to the control group.

Conclusions

The results showed that the investigated nonlinear features of resting state EEG are quite group-specific and also specific to the emotional state.

Significance

The investigated features could serve to diagnose emotional impairments.

A Novel EEG Based Spectral Analysis of Persistent Brain Function Alteration in Athletes with Concussion History

The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Detecting deficits are vital in making a decision about the treatment plan as it can persist one year or more following a brain injury. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits combining EEG analysis with three standard post-concussive assessment tools. Data were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency (Hjorth Parameters) and nonlinear features (approximate entropy and Hurst exponent) for the first time to explore post-concussive deficits. Besides traditional frequency-band analysis, we also presented a new individual frequency-based approach for EEG assessment. While EEG analysis exhibited significant discrepancies between the groups, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlights that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management.

Brain and cognitive reserve: Translation via network control theory

Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive "reserve," associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention.

Applying fMRI complexity analyses to the single subject: a case study for proposed neurodiagnostics

Nonlinear dynamic tools have been statistically validated at the group level to identify subtle differences in system wide regulation of brain meso-circuits, often increasing clinical sensitivity over conventional analyses alone. We explored the feasibility of extracting information at the single-subject level, illustrating two pairs of healthy individuals with psychological differences in stress reactivity. We applied statistical and nonlinear dynamic tools to capture key characteristics of the prefrontal-limbic loop. We compared single subject results with statistical results for the larger group. We concluded that complexity analyses may identify important differences at the single-subject level, supporting their potential towards neurodiagnostic applications.

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

BACKGROUND

Poststroke depression (PSD) is one of the most common emotional disorders affecting post-stroke patients. However, the neurophysiological mechanism remains elusive. This study was aimed to study the relationship between complexity of neural electrical activity and PSD.

METHODS

Resting state eye-closed electroencephalogram (EEG) signals of 16 electrodes were recorded in 21 ischemic poststroke depression (PSD) patients, 22 ischemic poststroke non-depression (PSND) patients and 15 healthy controls (CONT). Lempel-Ziv Complexity (LZC) was used to evaluate changes in EEG complexity in PSD patients. Statistical analysis was performed to explore difference among different groups and electrodes. Correlation between the severity of depression (HDRS) and EEG complexity was determined with pearson correlation coefficients. Receiver operating characteristic (ROC) and binary logistic regression analysis were conducted to estimate the discriminating ability of LZC for PSD in specificity, sensitivity and accuracy.

RESULTS

PSD patients showed lower neural complexity compared with PSND and CONT subjects in the whole brain regions. There was no significant difference among different brain regions, and no interactions between group and electrodes. None of the LZC significantly correlated with overall depression severity or differentiated symptom severity of 7 items in PSD patients, but in stroke patients, significant correlation was found between HDRS and LZC in the whole brain regions, especially in frontal and temporal. LZC parameters used for PSD recognition possessed more than 85% in specificity, sensitivity and accuracy, suggesting the feasibility of LZC to serve as screening indicators for PSD. Increased slow wave rhythms were found in PSD patients and clearly correlation was confirmed between neuronal complexity and spectral power of the four EEG rhythms.

LIMITATIONS

Lesion location of stroke patients in the study distributed in different brain regions, and most of the PSD patients were mild or moderate in depressive severity.

CONCLUSIONS

Compared with conventional spectral analysis, complexity of neural activity using LZC was more sensitive and stationary in the measurement of abnormal brain activity in PSD patients and may offer a potential approach to facilitate clinical screening of this disease.

[To err is human? Interests of chaotic models to study adult psychiatric disorders and developmental disorders]

BACKGROUND

Many clinical and biological parameters have nonlinear chaotic fluctuations. These variations result in unexpected pseudo-random transitions. In these models, few risk factors can lead to unexpected phenomena if oscillations and self-reinforcement patterns occur. Complex rhythms could ease the ability of a physiological system to adapt and react quickly to a constantly changing environment.

OBJECTIVES

It has been proposed that several psychiatric disorders and developmental disorders are characterized by a loss of complex rhythm in favor of a more organized pattern. We examine evidence to support these assumptions in literatures.

METHODS

We performed a literature review of the main computerized databases (Medline, PubMed) and manual searches of the literature concerning non dynamic rhythms in time series analysis, in adults with psychiatric disorder and children with developmental disorder. These results were interpreted through a developmental approach that highlights the role of the learning process in the emergence of abilities.

RESULTS

Analysis of clinical scores and electroencephalographic data have found that subjects with bipolar disorder or schizophrenia, tested over a time series, have lower chaotic rhythms compared with healthy subjects. Growing children share several properties of a complex system: the interdependence of developmental axes (motor, emotional, language, social skills), multiple hierarchical levels (i.e. genetic, biological, environmental, and cultural), the two-way transactions between the child and his environment, and the sensitivity to initial conditions. This could explain the difficulty to predict the emergence of abilities or the long-term prognosis of impairment in children. This limitation is not only due to errors in the explanatory model or the lack of explanatory variable. It is also caused by instability, which is a core characteristic of a chaotic system.

CONCLUSION

The study of chaotic rhythms in time-series clinical and nonclinical data (e.g. EEG, functional neuroimaging) could improve the prediction of an acute event, such as relapse of mood disorder. Moreover, the complex rhythms in children may play a major part in synchronicity during interactions with a caregiver, held as essential for later development of self-regulation skills, such as emotional stability.

Computer-Aided Diagnosis of Depression Using EEG Signals

The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.

Effects of electroconvulsive therapy on neural complexity in patients with depression: report of three cases

BACKGROUND

The exact neurophysiological mechanism of electroconvulsive therapy (ECT) for treating patients with depression remains elusive. Results of previous neurophysiological studies support the hypothesis that aberrant functional connectivity underlies the pathophysiology of depression, which engenders abnormal electroencephalogram (EEG) complexity.

METHODS

Recently developed multiscale entropy analysis, which has underpinned aberrant functional connectivity in mental disorders, was introduced to explore changes in EEG complexity occurring with ECT in three patients with depression.

RESULTS

All patients demonstrated a decrease in EEG complexity, especially at higher frequencies. This decrease was associated with improvement of depressive symptoms.

LIMITATIONS

The generalizability of our findings was constrained because of the small sample size and lack of a comparison with healthy controls.

CONCLUSIONS

The decrease in EEG complexity with ECT might be a result of amelioration of functional connectivity in the brain of a depressed patient. Multiscale entropy analysis might be a useful analytical method to elucidate neurophysiological mechanisms and evaluate the therapeutic efficacy of ECT in depression.

The association between the disruption of motor imagery and the number of depressive episodes of major depression

BACKGROUND

Mental rotation performance may be used as an index of mental slowing or bradyphrenia, and may reflect, in particular, speed of motor preparation. Previous studies suggest depressive patients present the correlates of impaired behavioural performance for mental rotation and psychomotor disturbance. The aim of this study is to compare the mental rotation abilities of patients with a first episode of depression, recurrent depression and healthy control subjects with regard to hand tasks.

METHODS

We tested 32 first episode of depression, 38 recurrent depression and 36 healthy control subjects by evaluating the performance of depressed patients with regard to the hand mental rotation tasks.

RESULTS

First, the first episode and recurrent depression subjects were significantly slower and made more errors than controls in mentally rotating hands. Second, the first depressive episode but not the recurrent depression displayed the same pattern of response times to stimuli at various orientations relative to control subjects in the hand task. Third, in particular, recurrent depression subjects were significantly slower and made more errors during the mental transformation of hands than first depressive episode relative to control subjects and the differences were significantly larger in female than male subjects in the mental rotation hand task.

LIMITATIONS

Patients were on antidepressant medication.

CONCLUSIONS

These results suggest that the impaired behavioural performance for mental representation processing are related to the number of previous episodes. Moreover, the recurrent major depressive episodes may contribute to the reinforcement of cognitive impairments and further the development or maintenance of mental representation dysfunctions, especially in female patients. A deficit on mental rotation in the depressive patients may be potential biomarkers for recurrence chronically.

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.

Dynamic processes in regulation and some implications for biofeedback and biobehavioral interventions

Systems theory has long been used in psychology, biology, and sociology. This paper applies newer methods of control systems modeling for assessing system stability in health and disease. Control systems can be characterized as open or closed systems with feedback loops. Feedback produces oscillatory activity, and the complexity of naturally occurring oscillatory patterns reflects the multiplicity of feedback mechanisms, such that many mechanisms operate simultaneously to control the system. Unstable systems, often associated with poor health, are characterized by absence of oscillation, random noise, or a very simple pattern of oscillation. This modeling approach can be applied to a diverse range of phenomena, including cardiovascular and brain activity, mood and thermal regulation, and social system stability. External system stressors such as disease, psychological stress, injury, or interpersonal conflict may perturb a system, yet simultaneously stimulate oscillatory processes and exercise control mechanisms. Resonance can occur in systems with negative feedback loops, causing high-amplitude oscillations at a single frequency. Resonance effects can be used to strengthen modulatory oscillations, but may obscure other information and control mechanisms, and weaken system stability. Positive as well as negative feedback loops are important for system function and stability. Examples are presented of oscillatory processes in heart rate variability, and regulation of autonomic, thermal, pancreatic and central nervous system processes, as well as in social/organizational systems such as marriages and business organizations. Resonance in negative feedback loops can help stimulate oscillations and exercise control reflexes, but also can deprive the system of important information. Empirical hypotheses derived from this approach are presented, including that moderate stress may enhance health and functioning.

Oscillatory serotonin function in depression

Oscillations in brain activities with periods of minutes to hours may be critical for normal mood behaviors. Ultradian (faster than circadian) rhythms of mood behaviors and associated central nervous system activities are altered in depression. Recent data suggest that ultradian rhythms in serotonin (5HT) function also change in depression. In two separate studies, 5HT metabolites in cerebrospinal fluid (CSF) were measured every 10 min for 24 h before and after chronic antidepressant treatment. Antidepressant treatments were associated with enhanced ultradian amplitudes of CSF metabolite levels. Another study used resting-state functional magnetic resonance imaging (fMRI) to measure amplitudes of dorsal raphé activation cycles following sham or active dietary depletions of the 5HT precursor (tryptophan). During depletion, amplitudes of dorsal raphé activation cycles increased with rapid 6 s periods (about 0.18 Hz) while functional connectivity weakened between dorsal raphé and thalamus at slower periods of 20 s (0.05 Hz). A third approach studied MDMA (ecstasy, 3,4-methylenedioxy-N-methylamphetamine) users because of their chronically diminished 5HT function compared with non-MDMA polysubstance users (Karageorgiou et al., 2009). Compared with a non-MDMA using cohort, MDMA users showed diminished fMRI intra-regional coherence in motor regions along with altered functional connectivity, again suggesting effects of altered 5HT oscillatory function. These data support a hypothesis that qualities of ultradian oscillations in 5HT function may critically influence moods and behaviors. Dysfunctional 5HT rhythms in depression may be a common endpoint and biomarker for depression, linking dysfunction of slow brain network oscillators to 5HT mechanisms affected by commonly available treatments. 5HT oscillatory dysfunction may define illness subtypes and predict responses to serotonergic agents. Further studies of 5HT oscillations in depression are indicated.

Time dependency of psychotherapeutic exchanges: the contribution of the theory of dynamic systems in analyzing process

This paper provides a general framework for the use of Theory of Dynamic Systems (TDS) in the field of psychotherapy research. Psychotherapy is inherently dynamic, namely a function of time. Consequently, the improvement of construct validity and clinical relevance of psychotherapy process research require the development of models of investigation allowing dynamic mappings of clinical exchange. Thus, TDS becomes a significant theoretical and methodological reference. The paper focuses two topics. First, the main concepts of TDS are briefly introduced together with a basic typology of approaches developed within this domain. Second, we propose a repertoire of investigation strategies that can be used to capture the dynamic nature of clinical exchange. In this way we intend to highlight the feasibility and utility of strategies of analysis informed by TDS.

The possibility of evidence-based psychiatry: depression as a case

Considering psychiatry as a medical discipline, a diagnosis identifying a disorder should lead to an effective therapy. Such presumed causality is the basis of evidence-based psychiatry. We examined the strengths and weaknesses of research onto the causality of relationship between diagnosis and therapy of major depressive disorder and suggest what could be done to strengthen eventual claims on causality. Four obstacles for a rational evidence-based psychiatry were recognised. First, current classification systems are scientifically nonfalsifiable. Second, cerebral processes are—at least to some extent—nondeterministic, i.e. they are random, stochastic and/or chaotic. Third, the vague or lack of relationship between therapeutic regimens and suspected pathogenesis. Fourth, the inadequacy of tools to diagnose and delineate a functional disorder. We suggest a strategy to identify diagnostic prototypes that are characterised by a limited number of parameters (symptoms, markers and other characteristics). A prototypical diagnosis that may either support or reject particular elements of current diagnostic systems. Nevertheless, one faces the possibility that psychiatry will remain a relatively weak evidence-based medical discipline.

A decentralized adaptive robust method for chaos control

This paper presents a control strategy, which is based on sliding mode control, adaptive control, and fuzzy logic system for controlling the chaotic dynamics. We consider this control paradigm in chaotic systems where the equations of motion are not known. The proposed control strategy is robust against the external noise disturbance and system parameter variations and can be used to convert the chaotic orbits not only to the desired periodic ones but also to any desired chaotic motions. Simulation results of controlling some typical higher order chaotic systems demonstrate the effectiveness of the proposed control method.

Systems biology and its application to the understanding of neurological diseases

Recent advances in molecular biology, neurobiology, genetics, and imaging have demonstrated important insights about the nature of neurological diseases. However, a comprehensive understanding of their pathogenesis is still lacking. Although reductionism has been successful in enumerating and characterizing the components of most living organisms, it has failed to generate knowledge on how these components interact in complex arrangements to allow and sustain two of the most fundamental properties of the organism as a whole: its fitness, also termed its robustness, and its capacity to evolve. Systems biology complements the classic reductionist approaches in the biomedical sciences by enabling integration of available molecular, physiological, and clinical information in the context of a quantitative framework typically used by engineers. Systems biology employs tools developed in physics and mathematics such as nonlinear dynamics, control theory, and modeling of dynamic systems. The main goal of a systems approach to biology is to solve questions related to the complexity of living systems such as the brain, which cannot be reconciled solely with the currently available tools of molecular biology and genomics. As an example of the utility of this systems biological approach, network-based analyses of genes involved in hereditary ataxias have demonstrated a set of pathways related to RNA splicing, a novel pathogenic mechanism for these diseases. Network-based analysis is also challenging the current nosology of neurological diseases. This new knowledge will contribute to the development of patient-specific therapeutic approaches, bringing the paradigm of personalized medicine one step closer to reality.

Altered perception of apparent motion in schizophrenia spectrum disorder

Apparent motion (AM), the Gestalt perception of motion in the absence of physical motion, was used to study perceptual organization and neurocognitive binding in schizophrenia. Associations between AM perception and psychopathology as well as meaningful subgroups were sought. Circular and stroboscopic AM stimuli were presented to 68 schizophrenia spectrum patients and healthy participants. Psychopathology was measured using the Positive and Negative Syndrome Scale (PANSS). Psychopathology was related to AM perception differentially: Positive and disorganization symptoms were linked to reduced gestalt stability; negative symptoms, excitement and depression had opposite regression weights. Dimensions of psychopathology thus have opposing effects on gestalt perception. It was generally found that AM perception was closely associated with psychopathology. No difference existed between patients and controls, but two latent classes were found. Class A members who had low levels of AM stability made up the majority of inpatients and control subjects; such participants were generally young and male, with short reaction times. Class B typically contained outpatients and some control subjects; participants in class B were older and showed longer reaction times. Hence AM perceptual dysfunctions are not specific for schizophrenia, yet AM may be a promising stage marker.

Dinâmica não-linear e psiquiatria: a natureza dinâmica das doenças mentais

A psiquiatria moderna adotou a postura reducionista de associar psicopatologia à fisiologia sináptica. Isto produziu avanços importantes na farmacoterapia dos transtornos mentais. Entretanto, a introdução de princípios da dinâmica não-linear promoveu uma mudança do enfoque sináptico para o enfoque das vias específicas patologicamente autônomas envolvidas nos fenômenos psicopatológicos. Esta tendência teve início nos anos 1990 e trouxe um conceito mais sistêmico de fisiopatologia e tratamento em psiquiatria.

On the meaning and interpretation of global descriptors of brain electrical activity. Including a reply to X. Pei et al

Global descriptors of the brain's electrical activity, Sigma, Phi, and Omega, provide a comprehensive characterisation of brain functional states. Recently, Pei et al. [Pei, X., Zheng, C., Zhang, A., Duan, F., Bin, G., 2005. Discussion on "Towards a quantitative characterisation of functional states of the brain: from the nonlinear methodology to the global linear description" by J. Wackermann. Int. J. Psychophysiol. 56, 201-207] discussed the effects of signal power on the global measure of spatial complexity, Omega, and suggested a modification consisting in epoch-wise and channel-wise normalisation of input data to unit power. In the present paper, the basic principles of the global approach are reviewed, and the issues of Pei et al.'s approach are assessed. The original and the modified measures of spatial complexity are compared in two case studies. Numerical simulation shows that both methods veridically estimate small numbers of signal sources, but systematically underestimate as the number increases; the modified method yields a minor relative improvement. A study on real EEG data shows that the two measures sensibly differ only where artefactual inhomogeneities in channel variances affect the data; a combined procedure, consisting in record-wise equalisation of channel variances before Omega calculations, is suggested as the optimal strategy. Differences between the original objectives of the global methodology and the proposed modifications are pointed out and critically discussed.

Approximate entropy of self-reported mood prior to episodes in bipolar disorder

BACKGROUND

Approximate entropy (ApEn) measures regularity in time series data, while traditional linear statistics measure variability. Using self-reported mood data from patients with bipolar disorder, this preliminary study addressed whether ApEn could distinguish (i) the 60 days prior to the start of a manic or depressed episode from the 60 days prior to a month of euthymia, and (ii) the 60 days prior to a manic episode from the 60 days prior to a depressed episode.

METHODS

Self-reported mood data from 49 outpatients with bipolar disorder receiving standard treatment were analysed. The data contained 27 episodes (12 manic and 15 depressed), and 43 periods of 1 month of euthymia. For the 60 days prior to episode or euthymia, the ApEn, linear statistics and the correlation between linear and non-linear measures were calculated.

RESULTS

ApEn was significantly greater in the 60 days prior to a manic or depressive episode than the 60 days prior to a month of euthymia. The onset of an episode was associated with greater irregularity in mood. Variability was also significantly larger and correlated with ApEn. ApEn was significantly greater in the 60 days prior to a manic episode than in the 60 days prior to a depressed episode, whereas measures of variability were not significantly different. Mood in the 60 days prior to mania was more irregular than prior to depression.

CONCLUSIONS

Non-linear measures may complement traditional linear measures in the analysis of longitudinal data in bipolar disorder. A larger study is indicated.

Computational neuropharmacology: dynamical approaches in drug discovery

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

Nonlinear dynamical analysis of EEG and MEG: review of an emerging field

Many complex and interesting phenomena in nature are due to nonlinear phenomena. The theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a stage, where it becomes possible to study self-organization and pattern formation in the complex neuronal networks of the brain. One approach to nonlinear time series analysis consists of reconstructing, from time series of EEG or MEG, an attractor of the underlying dynamical system, and characterizing it in terms of its dimension (an estimate of the degrees of freedom of the system), or its Lyapunov exponents and entropy (reflecting unpredictability of the dynamics due to the sensitive dependence on initial conditions). More recently developed nonlinear measures characterize other features of local brain dynamics (forecasting, time asymmetry, determinism) or the nonlinear synchronization between recordings from different brain regions. Nonlinear time series has been applied to EEG and MEG of healthy subjects during no-task resting states, perceptual processing, performance of cognitive tasks and different sleep stages. Many pathologic states have been examined as well, ranging from toxic states, seizures, and psychiatric disorders to Alzheimer's, Parkinson's and Cre1utzfeldt-Jakob's disease. Interpretation of these results in terms of 'functional sources' and 'functional networks' allows the identification of three basic patterns of brain dynamics: (i) normal, ongoing dynamics during a no-task, resting state in healthy subjects; this state is characterized by a high dimensional complexity and a relatively low and fluctuating level of synchronization of the neuronal networks; (ii) hypersynchronous, highly nonlinear dynamics of epileptic seizures; (iii) dynamics of degenerative encephalopathies with an abnormally low level of between area synchronization. Only intermediate levels of rapidly fluctuating synchronization, possibly due to critical dynamics near a phase transition, are associated with normal information processing, whereas both hyper-as well as hyposynchronous states result in impaired information processing and disturbed consciousness.

Treatment enhances ultradian rhythms of CSF monoamine metabolites in patients with major depressive episodes

Unipolar and bipolar depressions show abnormal behavioral manifestations of ultradian (less than 24 h) rhythms, but abnormal rhythms of the central neurotransmitters thought to be important for depression pathophysiology (eg dopamine (DA) and serotonin (5-HT)) have not been shown in this time frame. Since antidepressant treatments normalize disrupted rhythms in depression (eg rapid-eye-movement sleep and hormonal rhythms), we hypothesized that depression-related changes in ultradian oscillations of DA and 5-HT might be revealed during antidepressant treatment. Cerebrospinal fluid (CSF) samples collected q10 min for 24 h in 13 patients experiencing major depressive episodes (MDE) before and after treatment for 5 weeks with sertraline or bupropion were assayed for levels of homovanillic acid (HVA) and 5-hydroxyindoleacetic acid (5-HIAA), and their ratio was calculated. Data were analyzed in the frequency domain using Fourier transforms and multivariate permutation testing. Antidepressant treatments were associated with decreased variance for 5-HIAA, increased variance for HVA, and markedly increased variance for the HVA : 5-HIAA ratio (p<0.05, p<0.02, and p<0.003, respectively). With treatment, the correlations between 5-HIAA and HVA weakened (p=0.06). Power spectral density (PSD-the Fourier magnitude squared) of the 5-HIAA signals at periods of 1.75 and 3.7 h (both p<0.05) decreased, while circadian cycling of HVA levels (p<0.05) and of the ratio (p<0.005) increased after treatment. The PSD of the full-length HVA : 5-HIAA ratio series after treatment increased in rapid variability (20-103 min periods, p<0.05). Spectrographic windowing demonstrated a focal span of enhanced HVA : 5-HIAA ratio variability following antidepressant treatment, in an approximately 84-min period through the evening (p<0.05). Periodic neurotransmitter relationships in depressed patients were altered by treatment in this analysis of a small data set. This may represent a baseline abnormality in the regulation of periodic functions involved in the depression pathophysiology, but it could also be due to an unrelated antidepressant effect. Further studies including comparisons with healthy subject data are in progress.

Psychobiology of altered states of consciousness

The article reviews the current knowledge regarding altered states of consciousness (ASC) (a) occurring spontaneously, (b) evoked by physical and physiological stimulation, (c) induced by psychological means, and (d) caused by diseases. The emphasis is laid on psychological and neurobiological approaches. The phenomenological analysis of the multiple ASC resulted in 4 dimensions by which they can be characterized: activation, awareness span, self-awareness, and sensory dynamics. The neurophysiological approach revealed that the different states of consciousness are mainly brought about by a compromised brain structure, transient changes in brain dynamics (disconnectivity), and neurochemical and metabolic processes. Besides these severe alterations, environmental stimuli, mental practices, and techniques of self-control can also temporarily alter brain functioning and conscious experience.

Emotional information processing in first and recurrent major depressive episodes

Depressive states are classically associated to increased sensitivity to negative events. However this hypersensitivity may not be stable in time, being absent in remission periods or further reinforced with recurrent depressive episodes, or may concern positive stimuli instead, e.g. in young depressive patients. To study the evolution of the processing of emotional information in depression we recorded late components of evoked potentials in first-episode and recurrent depressed patients before and after recovery. We used a visual attentional paradigm manipulating the processing of emotional information. Subjects first counted words with positive valence, and then words with negative valence from lists of usual words. The results showed that recurrent patients had increased P300 amplitudes for negative words selection only in negative words counting situation, while first-episode patients had decreased P300 amplitudes for positive words selection. After clinical improvement, the negative biases in recurrent patients group disappeared but P300 amplitudes of first-episode patients remained significantly low for positive words. First-episode depressed patients show a selective impairment for positive stimuli, with decreased response to pleasant stimuli, while recurrent depressive subjects show signs of hyperesthesia for negative stimuli. These results suggest that responses to emotional stimuli in word processing are related to the duration of the mood disorders.

Toward a neurodynamical understanding of ictogenesis

Although considerable information on cellular and network mechanisms of epilepsy exists, it is still not understood why, how, and when the transition from interictal to ictal state takes place. The authors review their work on nonlinear EEG analysis and provide consistent evidences that dynamical changes in the neural activity allows the characterization of a preictal state several minutes before seizure onset. This new neurodynamical approach of ictogenesis opens new perspectives for studying the basic mechanisms in epilepsy as well as for possible therapeutic interventions.

Emergent oscillations in a mathematical model of the human menstrual cycle

BACKGROUND

The aim of this study was to develop a mathematical model of the hypothalamo-pituitary-gonadal axis that would reflect available data in humans.

METHODS

A model of hormonal relationships at the early follicular and midluteal phases of the human menstrual cycle is proposed.

FINDINGS

Two distinct temporal patterns of oscillatory behavior have been demonstrated for both pituitary and gonadal steroids in the early follicular phase: first, rapid oscillations in gonadotropin releasing hormone, follicle stimulating hormone and luteinizing hormone (Q approximate to 1 hour) that were an immediate consequence of the programmed equations. Second, there were slower, undulating, emergent rhythms in luteinizing hormone and follicle stimulating hormone, and also in estrogen, having oscillatory periods of 2-12 hours. There was also a longer-period (Q2-3 days) emergent rhythm in progesterone. In the mid-luteal phase, estrogen and progesterone rhythms were correlated, and all hormones showed an approximately 6-hour periodicity.

CONCLUSIONS

To our knowledge, the oscillatory behavior of peripheral sex steroids in the follicular phase has not been previously noted.

The algorithmic complexity of multichannel EEGs is sensitive to changes in behavior

Symbolic measures of complexity provide a quantitative characterization of the sequential structure of symbol sequences. Promising results from the application of these methods to the analysis of electroencephalographic (EEG) and event-related brain potential (ERP) activity have been reported. Symbolic measures used thus far have two limitations, however. First, because the value of complexity increases with the length of the message, it is difficult to compare signals of different epoch lengths. Second, these symbolic measures do not generalize easily to the multichannel case. We address these issues in studies in which both single and multichannel EEGs were analyzed using measures of signal complexity and algorithmic redundancy, the latter being defined as a sequence-sensitive generalization of Shannon's redundancy. Using a binary partition of EEG activity about the median, redundancy was shown to be insensitive to the size of the data set while being sensitive to changes in the subject's behavioral state (eyes open vs. eyes closed). The covariance complexity, calculated from the singular value spectrum of a multichannel signal, was also found to be sensitive to changes in behavioral state. Statistical separations between the eyes open and eyes closed conditions were found to decrease following removal of the 8- to 12-Hz content in the EEG, but still remained statistically significant. Use of symbolic measures in multivariate signal classification is described.

Time series models of symptoms in schizophrenia

The symptom courses of 84 schizophrenia patients (mean age: 24.4 years; mean previous admissions: 1.3; 64% males) of a community-based acute ward were examined to identify dynamic patterns of symptoms and to investigate the relation between these patterns and treatment outcome. The symptoms were monitored by systematic daily staff ratings using a scale composed of three factors: psychoticity, excitement, and withdrawal. Patients showed moderate to high symptomatic improvement documented by effect size measures. Each of the 84 symptom trajectories was analyzed by time series methods using vector autoregression (VAR) that models the day-to-day interrelations between symptom factors. Multiple and stepwise regression analyses were then performed on the basis of the VAR models. Two VAR parameters were found to be associated significantly with favorable outcome in this exploratory study: 'withdrawal preceding a reduction of psychoticity' as well as 'excitement preceding an increase of withdrawal'. The findings were interpreted as generating hypotheses about how patients cope with psychotic episodes.

Autobiographical memory in major depression: a comparison between first-episode and recurrent patients

Autobiographical memory in depression is characterized by an increase in general memory evocation. The aim of this study is to compare autobiographical memory in patients with a first depressive episode and in recurrent patients before and after recovery, using Williams' and Scott's autobiographical memory test. Our results show an increase of the number of general memories only with positive cue words in both groups of patients during the depressive episode. After clinical improvement, this specificity remains in recurrent patients who, in addition, recall more general memories for negative words. By contrast, patients with a first depressive episode are no longer different from controls. These results show both an overgeneralization and a deficit in positive memory access during the depressive episode, whatever the number of previous episodes. Moreover, recurrence chronically modifies access to emotional memories.

Dynamic analysis of inter-words time intervals: a method to analyze the structure of communicative signals

Speech analyses are usually focused on words as signifiers ignoring inter-words time intervals (IWIs), which are related to the 'form' of speech, rather than to its 'content'. Applying the method of power spectrum analysis to inter-vocalizations time intervals of bird singing, underlying periodic processes were detected. In contrast, human IWIs revealed non-periodicity, which may be random or chaotic. To differentiate between these two possibilities, the non-linear dynamic methods of unstable periodic orbits and correlation dimension were applied to show that IWIs are characterized by a low dimensional chaotic attractor. Its correlation dimension of 3.2 +/- 1.1 suggests a minimum number of four variables underlying the system. The methods developed in the present communication can be further applied: (a) for the measurement of specific alterations in the processes underlying the form of speech in human disorders, i.e., schizophrenia, (b) for the assessment of normal and pathological developmental aspects of speech processes in children; (c) for comparing communicative signals between humans and other species.

Chaos in the brain: a short review alluding to epilepsy, depression, exercise and lateralization

Electroencephalograms (EEGs) reflect the electrical activity of the brain. Even when they are analyzed from healthy individuals, they manifest chaos in the nervous system. EEGs are likely to be produced by a nonlinear system, since a nonlinear system with at least 3 degrees of freedom (or state variables) may exhibit chaotic behavior. Furthermore, such systems can have multiple stable states governed by "chaotic" ("strange") attractors. A key feature of chaotic systems is the presence of an infinite number of unstable periodic fixed points, which are found in spontaneously active neuronal networks (e.g., epilepsy). The brain has chemicals called neurotransmitters that convey the information through the 10(16) synapses residing there. However, each of these neurotransmitters acts through various receptors and their numerous subtypes, thereby exhibiting complex interactions. Albeit in epilepsy the role of chaos and EEG findings are well proven, in another condition, i.e., depression, the role of chaos is slowly gaining ground. The multifarious roles of exercise, neurotransmitters and (cerebral) hemispheric lateralization, in the case of depression, are also being established. The common point of reference could be nonlinear dynamics. The purpose of this review is to study those nonlinear/chaotic interactions and point towards new theoretical models incorporating the oscillation caused by the same neurotransmitter acting on its different receptor subtypes. This may lead to a better understanding of brain neurodynamics in health and disease.

Characterizing neurodynamic changes before seizures

The study of dynamic changes in neural activity preceding epileptic seizure allows the characterization of a preictal state several minutes before seizure onset. This opens up new perspectives for studying the mechanisms of epileptogenesis as well as for possible therapeutic interventions, which represent a major breakthrough. In this review the authors present and discuss the results from their group in this domain using nonlinear analysis of brain signals, as well as the limitations of this topic and current questions.

Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson's disease

OBJECTIVES

Modifications of brain activity in the early stages of Parkinson's disease (PD) are difficult to detect using electroencephalography (EEG) signals and are often biased by L-DOPA treatment. We compare here the performances of both linear and non-linear methods in differentiating EEG of L-DOPA naive PD patients from that of control subjects.

METHODS

Resting multichannel EEG (20 electrodes, 30 s epochs) of 9 patients with PD in Hoehn and Yahr stages 1-2 (4 women, 5 men, mean age 54.3 years, range 48-63 years) were compared with those of 9 control subjects (7 women, two men, mean age 51.3 years, range 43-61 years). The following measurements were computed: theta-, alpha- and beta-band relative powers constituted the linear indices; localized entropy, slope asymmetry and number of non-linear EEG segments constituted the non-linear indices.

RESULTS

In the case of linear quantification, only a decrease in the beta-band was observed for patients. Significant non-linear structures were observed in our EEG data. Non-linear quantifiers demonstrate an increase in entropy and in the number of non-linear EEG segments for the patients.

CONCLUSIONS

Changes in EEG dynamics observed here in L-DOPA naive PD patients may represent early signs of cortical dysfunction produced by subcortical dopamine depletion.

Effects of noise on different disease states of recurrent affective disorders

BACKGROUND

Nonlinear dynamics are currently proposed to explain the course of recurrent affective disorders. Such a nonlinear disease model predicts complex interactions with stochastic influences, in particular, because both disease dynamics and stochastic influences, such as psychosocial stressors, will vary during the course of the disease. We approach this problem by investigating general effects of noise intensity on different disease states of a nonlinear model for recurrent affective disorders.

METHODS

A recently developed neurodynamic model is studied numerically.

RESULTS

Noise can cause unstructured randomness or can maximize periodic order. The frequency of episode occurrence can increase with noise but it can also remain unaffected or even can decrease. The observed effects, thereby, depend critically on both the noise intensity and the internal nonlinear dynamics of the disease model.

CONCLUSIONS

Our findings indicate that altered stochastic influences can significantly affect the outcome of a dynamic disease. To evaluate the effects of noise, it is essential to know about the underlying dynamics of respective disease states. Therefore, characterization of low-dimensional dynamics might become valuable for disease prediction and control.

Towards a quantitative characterisation of functional states of the brain: from the non-linear methodology to the global linear description

The paper traces the development of a global approach to the electric activity of the brain, from its roots in non-linear dynamical approach to the current state of art. The rationale of a three-dimensional system of global multichannel EEG descriptors (sigma, phi and omega) is provided and results obtained by means of the global descriptors in various application areas are summarised. Finally, arguments in favour of a global, 'holistic' assessment of brain functional states are presented. Definitions and properties of the global EEG descriptors are summarised in the Appendix.

Consequences of deterministic and random dynamics for the course of affective disorders

BACKGROUND

Uni- and bipolar affective disorders tend to be recurrent and progressive. Illness patterns can evolve from isolated episodes to more rapid, rhythmic, and "chaotic" mood patterns. Nonlinear deterministic dynamics are currently proposed to explain this progression. However, most natural systems are nonlinear and noisy, and cooperative behavior of possible clinical relevance can result.

METHODS

The latter issue has been studied with a mathematical model for progression of disease patterns in affective disorders.

RESULTS

Deterministic dynamics can reproduce a progression from stable, to periodic, to chaotic patterns. Noise increases the spectrum of dynamic behaviors, enhances the responsiveness to weak activations, and facilitates the occurrence of aperiodic patterns.

CONCLUSIONS

Noise might amplify subclinical vulnerabilities into disease onset and could induce transitions to rapid-changing dysrhythmic mood patterns. We suggest that noise-mediated cooperative behavior, including stochastic resonance, should be considered in appropriate models for affective illness.

Chaos theories and therapeutic commonalities among depression, Parkinson's disease, and cardiac arrhythmias

This report reviews and compares all therapies that have shown efficacy in depression and Parkinson's disease, although some are not in current use and others are at the experimental stage. They include pharmacological modification of neurotransmitter pathways, electroconvulsive therapy (ECT), sleep deprivation, psychosurgery, electrical stimulation through cerebral electrodes, and transcranial magnetic stimulation. Stemming from a pathophysiological model that stresses the brain as an open, complex, and nonlinear system, all therapies have been attributed a common mechanism of action. This report suggests that the therapeutic isomorphism is related to their ability to help the CNS deactivate cortical-subcortical circuits that are dysfunctionally coupled. These circuits are self-organized among neurons of their informational subsystem (rapid conduction) and modulating subsystem (slow conduction). Finally, this report extends the analysis and comparison of these therapies to some cardiac arrhythmias.