A novel electroencephalography-based tool for objective assessment of network dynamics activated by nociceptive stimuli.

BACKGROUND: Pain perception is typically assessed using subjective measures; an objective measure of the response to pain would be valuable. In this study, Brain Network Activation (BNA), a novel multivariate pattern analysis and scoring algorithm, was applied to event-related potentials (ERPs) elicited by cortical responses to brief heat stimuli. Objectives of this study were to evaluate the utility of BNA as a quantitative and qualitative measure of cortical response to pain. METHODS: Contact Heat Evoked Potentials (CHEPs) data were collected from 17 healthy, right-handed volunteers (10 M, 7F) using 5 different temperatures (35, 41, 46, 49 and 52 °C). A set of spatio-temporal activity patterns common to all the subjects in the group (Reference Brain Network Model; RBNM) was generated using the BNA algorithm, based on evoked responses at 52 °C. RESULTS: Frame by frame 'unfolding' of the brain network across time showed qualitative differences between responses to painful and non-painful stimuli. Brain network activation scores were shown to be a better indicator of the individual's sensitivity to pain when compared to subjective pain ratings. Additionally, BNA scores correlated significantly with temperature, demonstrated good test-retest reliability, as well as a high degree of sensitivity, specificity and accuracy in correctly categorizing subjects who reported stimuli as painful. CONCLUSIONS: These results may provide evidence that the multivariate analysis performed with BNA may be useful as a quantitative, temporally sensitive tool for assessment of pain perception.

Network dynamics predict improvement in working memory performance following donepezil administration in healthy young adults.

Attentional selection in the context of goal-directed behavior involves top-down modulation to enhance the contrast between relevant and irrelevant stimuli via enhancement and suppression of sensory cortical activity. Acetylcholine (ACh) is believed to be involved mechanistically in such attention processes. The objective of the current study was to examine the effects of donepezil, a cholinesterase inhibitor that increases synaptic levels of ACh, on the relationship between performance and network dynamics during a visual working memory (WM) task involving relevant and irrelevant stimuli. Electroencephalogram (EEG) activity was recorded in 14 healthy young adults while they performed a selective face/scene working memory task. Each participant received either placebo or donepezil (5mg, orally) on two different visits in a double-blinded study. To investigate the effects of donepezil on brain network dynamics we utilized a novel EEG-based Brain Network Activation (BNA) analysis method that isolates location-time-frequency interrelations among event-related potential (ERP) peaks and extracts condition-specific networks. The activation level of the network modulated by donepezil, reflected in terms of the degree of its dynamical organization, was positively correlated with WM performance. Further analyses revealed that the frontal-posterior theta-alpha sub-network comprised the critical regions whose activation level correlated with beneficial effects on cognitive performance. These results indicate that condition-specific EEG network analysis could potentially serve to predict beneficial effects of therapeutic treatment in working memory.

Introducing a novel approach of network oriented analysis of ERPs, demonstrated on adult attention deficit hyperactivity disorder.

OBJECTIVE: Introducing a network-oriented analysis method (brain network activation [BNA]) of event related potential (ERP) activities and evaluating its value in the identification and severity-grading of adult ADHD patients. METHODS: Spatio-temporal interrelations and synchronicity of multi-sited ERP activity peaks were extracted in a group of 13 ADHD patients and 13 control subjects for the No-go stimulus in a Go/No-go task. Participants were scored by cross-validation against the most discriminative ensuing group patterns and scores were correlated to neuropsychological evaluation scores. RESULTS: A distinct frontal-central-parietal pattern in the delta frequency range, dominant at the P3 latency, was unraveled in controls, while central activity in the theta and alpha frequency ranges predominated in the ADHD pattern, involving early ERP components (P1-N1-P2-N2). Cross-validation based on this analysis yielded 92% specificity and 84% sensitivity and individual scores correlated well with behavioral assessments. CONCLUSIONS: These results suggest that the ADHD group was more characterized by the process of exerting attention in the early monitoring stages of the No-go signal while the controls were more characterized by the process of inhibiting the response to that signal. SIGNIFICANCE: The BNA method may provide both diagnostic and drug development tools for use in diverse neurological disorders.

Forecasting epilepsy from the heart rate signal.

Information contained in the R-R interval series, specific to the pre-ictal period, was sought by applying an unsupervised fuzzy clustering algorithm to the N-dimensional phase space of N consecutive interval durations or the absolute value of duration differences. Data sources were individual, complex partial seizures of temporal-lobe epileptics and generalised seizures of rats rendered epileptic with hyperbaric oxygen. Forecasting success was 86% and 82% (zero false positives in resistant rats), respectively, at times ranging from 10 min to 30 s prior to seizure onset Although certain forecasting clusters predominated in the patient group and different ones predominated in the animal group, forecasting on the whole was seizure-specific. The high prediction sensitivity of this method, which matches that of EEG-based methods, seems promising. It is believed that an on-line version of the algorithm, trained on each patient's peri-ictal ECG, could serve as a basis for a simple seizure alarm system.

Detection of K-complexes in sleep EEG using CD-HMM.

In the current paper a new approach for K-complex detection using a continuous density hidden Markov model (CD-HMM) is presented. The system performance was evaluated in two manners. First using three seconds long segments of K-complexes and of background EEG (classification problem). Second using a whole night record and detecting the K complexes (detection problem). The fist test achieved an equal error rate of 7%. In the second test the system performance was compared to four trained scores that scored the signal independently. The performance of the algorithm was within the variance of the human scorers.

Functional connectivity and working memory in schizophrenia: an EEG study.

A leading hypothesis suggests that schizophrenic patients suffer from a disconnection syndrome. A failure in functional connectivity curtails the cortical integration and network activation needed to perform working memory tasks. Simulations with neural network models also indicate that connectivity is crucial for simulation of working memory asks. Multichannel EEG correlation-coefficient estimations are considered as a reliable measurement of connectivity patterns among cortical regions. In this study EEG samples are obtained selectively at the delay epochs of a delayed response working memory task. Results of correlation-coefficient estimations indicate a lack of statistically significant changes between non-task and task conditions in frontal, certain parietal, temporal and central channels. These findings propose that schizophrenics probably "fail" to activate the neural networks of the fronto-temporal regions. These are the networks involved in computation of the working memory task. Interestingly also good performers schizophrenics failed to activate these networks suggesting that the connectivity function is more relevant to the disorder than to task performance. If distinct deficits in cortical network activations would correlate with mental disorders it would be relevant to diagnosis and treatment of psychiatric disorders.

Touch feel illusion in schizophrenic patients.

BACKGROUND: The rubber hand illusion is a tactile sensation referred to as an alien limb. The illusion has been explained by a spurious reconciliation of visual and tactile inputs reflecting functional connectivity in the brain and was used to explore alterations of functional connectivity in schizophrenia. METHODS: The rubber hand illusion was achieved when two paintbrushes simultaneously stroke the hand of the subject hidden from vision by a screen, as well as an artificial hand placed in view of the subject. The rubber hand illusion was assessed with a questionnaire affirming or denying the occurrence of the illusion. RESULTS: Schizophrenic subjects felt the illusion stronger and faster then did normal control subjects. Some rubber hand illusion effects correlated with positive symptoms of schizophrenia but not with negative symptoms. CONCLUSIONS: Altered functional integration of environmental inputs could constitute the basis for erroneous interpretations of reality, such as delusions and hallucinations.

Autonomic dysregulation in panic disorder and in post-traumatic stress disorder: application of power spectrum analysis of heart rate variability at rest and in response to recollection of trauma or panic attacks.

Power spectral analysis (PSA) of heart rate variability (HRV) offers reliable assessment of cardiovascular autonomic responses, providing a 'window' onto the interaction of peripheral sympathetic and parasympathetic tone. Alterations in HRV are associated with various physiological and pathophysiological processes, and may contribute to morbidity and mortality. Previous studies of posttraumatic stress disorder (PTSD) found lower resting HRV in patients compared to controls, suggesting increased sympathetic and decreased parasympathetic tone. This article describes the analysis of HRV at rest and after psychological stress in panic disorder (PD) patients, in an enlarged sample of PTSD patients, and in healthy control subjects. Standardized heart rate (HR) analysis was carried out in 14 PTSD patients, 11 PD patients and 25 matched controls. ECG recordings were made while subjects were resting ('rest 1'), while recalling the trauma implicated in PTSD, or the circumstances of a severe panic attack, as appropriate ('recall'), and again while resting ('rest 2'). Controls were asked to recall a stressful life event during recall. While both patient groups had elevated HR and low frequency (LF) components of HRV at baseline (suggesting increased sympathetic activity), PTSD patients, unlike PD patients and controls, failed to respond to the recall stress with increases in HR and LF. HRV analysis demonstrates significant differences in autonomic regulation of PTSD and PD patients compared to each other and to control subjects. HRV analysis may augment biochemical studies of peripheral measures in these disorders.

The perception of rorschach inkblots in schizophrenia: a neural network model.

Schizophrenia is a psychiatric disorder characterized by a variety of cognitive deficits, including perceptual distortions and hallucinations. In recent years several studies have proposed that schizophrenia may involve a disturbance of "context". We have used a three layer neural network model constructed from an input layer followed by two computational layers to simulate responses of schizophrenic patients to the Rorschach test. In this test subjects respond to a set of ambiguous patterns created by ink blots on paper. Our model proposes that a disturbance of context caused by altered noise-to-signal ratio at the level of the single units, is responsible for schizophrenic responses to the Rorschach test. The assumption that catecholaminergic neurotransmitter systems regulate noise-to-signal ratio in cortical neurons constitutes a link between findings of altered neurotransmitter activity and deficits of cognitive functions requiring contextual integration in schizophrenia. The development of models for specific task deficits in schizophrenia could advance our insights regarding the neurological mechanisms underlying serious mental disorders such as schizophrenia.

Simulation of cognitive disturbances by a dynamic threshold semantic neural network.

A neural network model with dynamic thresholds, asymmetric connections, and clustered memories simulates spread activation that is hypothesized for semantic networks in the brain. By altering the parameters of the dynamic threshold a large range of disturbances can be generated in the model. These disturbances show metaphorical resemblance to certain general clinical descriptions of mental disturbances found in psychiatric patients engaged in various cognitive tasks. Even though the model is highly theoretical and metaphoric, it may help to gain certain insights into the relation between alterations of certain neural parameters, for example, thresholds and connectivity, and clinical symptoms in patients.

Errors in a nonlinear graphic-semantic mapping task resulting from lesions in Boltzmann machine: is it relevant to dyslexia?

One of the most fascinating aspects of brain research is the subject of language. As in many other cases, the malfunctions that occur in different persons for various reasons give us insight on the mechanisms that support our ability to talk, read and listen. Following the work of Plaut and associates, we deal with the dyslexia disorder, which is the overall name for a large number of reading disorders. A Boltzmann machine neural network scheme was trained to implement the nonlinear mapping task of graphic representation into semantic representation, which may model the brain sections responsible for the translation of a written word into meanings and syllables. After training, various types of lesions were applied and the performance of the network was tested in order to measure the effect of each lesion on the error rate and type distribution that were detected. The system's errors were classified into several categories and the distribution of errors between the categories was studied. Using the simulations, it is demonstrated that a finite scheduling process in the Boltzmann machine causes the distribution of the network's errors to be unique and different from its expected error distribution. The phenomenon is given a mathematical explanation rooted in the statistical mechanics basics of the Boltzmann machine. Test results suggest the localization of certain reading functions within the network. Comparison is made to relevant types of dyslexia and shows resemblance in major symptoms as well as in certain known side effects.

Nonstationary time series analysis by temporal clustering.

The object of this paper is to present a model and a set of algorithms for estimating the parameters of a nonstationary time series generated by a continuous change in regime. We apply fuzzy clustering methods to the task of estimating the continuous drift in the time series distribution and interpret the resulting temporal membership matrix as weights in a time varying, mixture probability distribution function (PDF). We analyze the stopping conditions of the algorithm to infer a novel cluster validity criterion for fuzzy clustering algorithms of temporal patterns. The algorithm performance is demonstrated with three different types of signals.

Brain organization and psychodynamics.

Any attempt to link brain neural activity and psychodynamic concepts requires a tremendous conceptual leap. Such a leap may be facilitated if a common language between brain and mind can be devised. System theory proposes formulations that may aid in reconceptualizing psychodynamic descriptions in terms of neural organizations in the brain. Once adopted, these formulations can help to generate testable predictions about brain-psychodynamic relations and thus significantly affect the future of psychotherapy.

Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering.

Dynamic state recognition and event-prediction are fundamental tasks in biomedical signal processing. We present a new, electroencephalogram (EEG)-based, brain-state identification method which could form the basis for forecasting a generalized epileptic seizure. The method relies on the existence in the EEG of a preseizure state, with extractable unique features, a priori undefined. We exposed 25 rats to hyperbaric oxygen until the appearance of a generalized EEG seizure. EEG segments from the preexposure, early exposure, and the period up to and including the seizure were processed by the fast wavelet transform. Features extracted from the wavelet coefficients were imputed to the unsupervised optimal fuzzy clustering (UOFC) algorithm. The UOFC is useful for classifying similar discontinuous temporal patterns in the semistationary EEG to a set of clusters which may represent brain-states. The unsupervised selection of the number of cluster overcomes the a priori unknown and variable number of states. The usually vague brain state transitions are naturally treated by assigning each temporal pattern to one or more fuzzy clusters. The classification succeeded in identifying several, behavior-backed, EEG states such as sleep, resting, alert and active wakefulness, as well as the seizure. In 16 instances a preseizure state, lasting between 0.7 and 4 min was defined. Considerable individual variability in the number and characteristics of the clusters may postpone the realization of an early universal epilepsy warning. University may not be crucial if using a dynamic version of the UOFC which has been taught the individual's normal vocabulary of EEG states and can be expected to detect unspecified new states.

Feature extraction and state identification in biomedical signals using hierarchical fuzzy clustering.

Many problems in the field of biomedical signal processing can be reduced to a task of state recognition and event prediction. Examples can be found in tachycardia detection from ECG signals, epileptic seizure or psychotic attack prediction from an EEG signal, and prediction of vehicle drivers falling asleep from both signals. The problem generally treats a set of ordered measurements and asks for the recognition of some patterns of observed elements that will forecast an event or a transition between two different states of the biological system. It is proposed to apply clustering methods to grouping discontinuous related temporal patterns of a continuously sampled measurement. The vague switches from one stationary state to another are naturally treated by means of fuzzy clustering. In such cases, an adaptive selection of the number of clusters (the number of underlying semi-stationary processes) can overcome the general non-stationary nature of biomedical signals and enable the formation of a warning cluster. The algorithm suggested for the clustering is a new recursive algorithm for hierarchical fuzzy partitioning. Each pattern can have a non-zero membership in more than one data subset in the hierarchy. A 'natural' and feasible solution to the cluster validity problem is suggested by combining hierarchical and fuzzy concepts. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class. The new method is applied to state recognition during recovery from exercise using the heart rate signal and to the forecasting of generalised epileptic seizures from the EEG signal.

ScaleNet--multiscale neural-network architecture for time series prediction.

The effectiveness of a multiscale neural-network (NN) architecture for the time series prediction of nonlinear dynamic systems has been investigated. The prediction task is simplified by decomposing different scales of past windows into different scales of wavelets (local frequencies), and predicting the coefficients of each scale of wavelets by means of a separate multilayer perceptron NN. The short-term history (short past windows) is decomposed into the lower scales of wavelet coefficients (high frequencies) which are utilized for "detailed" analysis and prediction, while the long-term history (long past window) is decomposed into higher scales of wavelet coefficients (low frequencies) that are used for the analysis and prediction of slow trends in the time series. These coordinated scales of time and frequency provides an interpretation of the series structures, and more information about the history of the series, using fewer coefficients than other methods. The prediction's results concerning all the different scales of time and frequencies are combined by another "expert" perceptron NN which learns the weight of each scale in the goal-prediction of the original time series. Each network is trained by the backpropagation algorithm using the Levenberg-Marquadt method. The weights and biases are initialized by a new clustering algorithm of the temporal patterns of the time series, which improves the prediction results as compared to random initialization. Three main sets of data were analyzed: the sunspots' benchmark, fluctuations in a farinfrared laser and a nonlinear numerically generated series. Taking the ultimate goal to be the accuracy of the prediction, we found that the suggested multiscale architecture outperforms the corresponding single-scale architectures. The employment of improved learning methods for each of the ScaleNet networks can further improve the prediction results.

Effects of auditory/visual and lexical/non-lexical comparisons on event-related potentials in a memory-scanning task.

A late parietal positivity (P3) and behavioural measures were studied during performance of a two-item memory-scanning task. Stimuli were digits presented as memorized items in one modality (auditory or visual) while the following probe, also a digit, was presented in the same or the other modality. In a separate set of experiments, P3 and behaviour were similarly studied using only visual stimuli that were either lexical (digits) or non-lexical (novel fonts with the same contours as the digits) to which subjects assigned numerical values. Reaction times (RTs) and P3 latencies were prolonged to non-lexical compared to lexical stimuli. Although RTs were longer to auditory than to visual stimuli, P3 latencies to memorized items were prolonged in response to visually compared to auditorily presented memorized items, and were further prolonged when preceding visual probes. P3 amplitudes were smaller to auditory than to visual stimuli, and were smaller for the second memorized item when lexical/non-lexical comparisons were involved. The most striking finding was scalp distribution variations indicating changes in relative contributions of brain structures involved in processing memorized items, according to the probes that followed. These findings are compatible, in general, with a phonological memorization, but they suggest that the process is modified by memorizing the item in the same terms as the expected probe that follows.

Multichannel wavelet-type decomposition of evoked potentials: model-based recognition of generator activity.

Scalp recording of electrical events allows the evaluation of human cerebral function, but contributions of the specific brain structures generating the recorded activity are ambiguous. This problem is ill-posed and cannot be solved without physiological constraints based on the spatio-temporal characteristics of the generators' activity. In our model-based analysis of evoked potentials for the purpose of generator activity detection, multichannel scalp-recorded signals are decomposed into a combination of wavelets, each of which can describe the neural mass coherent activity of cell assemblies. Elimination of contributions of specific generators and/or distributed background activity can produce physiologically motivated time-frequency filtering. The decomposition and filtering procedures are demonstrated by three examples; simulation of the surface manifestation of known intracranial generators; decomposition and reconstruction of auditory brainstem evoked potentials which reflect the differences among generators of these potentials; and cognitive components of evoked potentials which are diminished in the averaged recording but are clearly detected in single-trial signals.

Spatio-temporal multiple source localization by wavelet-type decomposition of evoked potentials.

Scalp recording of electrical events allows evaluation of human cerebral function, but contributions of the specific brain structures generating the recorded activity are ambiguous. This problem is ill-posed and cannot be solved without auxiliary physiological knowledge about the spatio-temporal characteristics of the generators' activity. In our source localization by model-based wavelet-type decomposition, scalp recorded signals are decomposed into a combination of wavelets, each of which may describe the coherent activity of a population of neurons. We chose the Hermite functions (derived from the Gaussian function to form mono-, bi- and triphasic wave forms) as the mathematical model to describe the temporal pattern of mass neural activity. For each wavelet we solve the inverse problem for two symmetrically positioned and oriented dipoles, one of which attains zero magnitude when a single source is more suitable. We use the wavelet to model the temporal activity pattern of the symmetrical dipoles. By this we reduce the dimension of inverse problem and find a plausible solution. Once the number and the initial parameters of the sources are given, we can apply multiple source localization to correct the solution for generators with overlapping activities. Application of the procedure to subcortical and cortical components of somatosensory evoked potentials demonstrates its feasibility.

Source estimation of auditory brainstem evoked potentials: comparison of 3CLT and dipole localization.

The generators of auditory brainstem evoked potentials (ABEPs) are generally agreed to be located between the auditory nerve and upper pons. Thus, they are all located within a few cm from the center of the head. Three-channel Lissajous' trajectory (3CLT) provides the amplitude and orientation of a centrally located equivalent dipole of surface recorded activity. Volume conductor theory predicts decreased spatial resolution of source estimation the deeper the source. In this study we compared source estimates obtained with 3CLT, using three orthogonal differential channels, with those obtained with two other source estimation methods: i) setting the generators at their known anatomical coordinates and calculating orientation and magnitude of the source (dipole localization method--DLM); ii) estimation of all source parameters, including the number of sources by wavelet-type decomposition, without assumptions on the location of the sources (multiple source estimate--MSE). 3CLT, DLM and MSE all converged on magnitudes and orientations that were not significantly different from each other, and locations that were within a few cm of each other. In conclusion, although 3CLT can only estimate a single, centrally located equivalent dipole, in the specific case of ABEPs, it provides the same information available from the more demanding source estimate methods. In addition to the considerable saving in recording channels, 3CLT is reference-independent and thus avoids ambiguities resulting from the choice of reference.