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

Cautionary Observations Concerning the Introduction of Psychophysiological Biomarkers into Neuropsychiatric Practice

The combination of statistical learning technologies with large databases of psychophysiological data has appropriately generated enthusiastic interest in future clinical applicability. It is argued here that this enthusiasm should be tempered with the understanding that significant obstacles must be overcome before the systematic introduction of psychophysiological measures into neuropsychiatric practice becomes possible. The objective of this study is to identify challenges to this effort. The nonspecificity of psychophysiological measures complicates their use in diagnosis. Low test-retest reliability complicates use in longitudinal assessment, and quantitative psychophysiological measures can normalize in response to placebo intervention. Ten cautionary observations are introduced and, in some instances, possible directions for remediation are suggested.

On the Operational Utility of Measures of Multichannel EEGs

Multichannel EEGs were obtained from healthy participants in the eyes-closed no-task condition and in the eyes-open condition (where the alpha component is typically abolished). EEG dynamics in the two conditions were quantified with two related binary Lempel–Ziv measures of the first principal component, and with three measures of integrated information, including the more recently proposed integrated synergy. Both integrated information and integrated synergy with model order p=1 had greater values in the eyes-closed condition. When the model order of integrated synergy was determined with the Bayesian Information Criterion, this pattern was reversed, and in line with the other measures, integrated synergy was greater in the eyes-open condition. Eyes-open versus eyes-closed separation was quantified by calculating the between-condition effect size. The Lempel–Ziv complexity of the first principal component showed greater separation than the measures of integrated information.

A Mathematical Approach to Correlating Objective Spectro-Temporal Features of Non-linguistic Sounds With Their Subjective Perceptions in Humans

Non-linguistic sounds (NLSs) are a core feature of our everyday life and many evoke powerful cognitive and emotional outcomes. The subjective perception of NLSs by humans has occasionally been defined for single percepts, e.g., their pleasantness, whereas many NLSs evoke multiple perceptions. There has also been very limited attempt to determine if NLS perceptions are predicted from objective spectro-temporal features. We therefore examined three human perceptions well-established in previous NLS studies ("Complexity," "Pleasantness," and "Familiarity"), and the accuracy of identification, for a large NLS database and related these four measures to objective spectro-temporal NLS features, defined using rigorous mathematical descriptors including stimulus entropic and algorithmic complexity measures, peaks-related measures, fractal dimension estimates, and various spectral measures (mean spectral centroid, power in discrete frequency ranges, harmonicity, spectral flatness, and spectral structure). We mapped the perceptions to the spectro-temporal measures individually and in combinations, using complex multivariate analyses including principal component analyses and agglomerative hierarchical clustering.

Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method

Kolmogorov-Chaitin complexity has long been believed to be impossible to approximate when it comes to short sequences (e.g. of length 5-50). However, with the newly developed coding theorem method the complexity of strings of length 2-11 can now be numerically estimated. We present the theoretical basis of algorithmic complexity for short strings (ACSS) and describe an R-package providing functions based on ACSS that will cover psychologists' needs and improve upon previous methods in three ways: (1) ACSS is now available not only for binary strings, but for strings based on up to 9 different symbols, (2) ACSS no longer requires time-consuming computing, and (3) a new approach based on ACSS gives access to an estimation of the complexity of strings of any length. Finally, three illustrative examples show how these tools can be applied to psychology.

Procedures for the Comparative Testing of Noninvasive Neuroassessment Devices

A sequential process for comparison testing of noninvasive neuroassessment devices is presented. Comparison testing of devices in a clinical population should be preceded by computational research and reliability testing with healthy populations, as opposed to proceeding immediately to testing with clinical participants. A five-step process is outlined as follows: 1. Complete a preliminary literature review identifying candidate measures. 2. Conduct systematic simulation studies to determine the computational properties and data requirements of candidate measures. 3. Establish the test-retest reliability of each measure in a healthy comparison population and the clinical population of interest. 4. Investigate the clinical validity of reliable measures in appropriately defined clinical populations. 5. Complete device usability assessment (weight, simplicity of use, cost effectiveness, ruggedness) only for devices and measures that are promising after steps 1 through 4 are completed. Usability may be considered throughout the device evaluation process but such considerations are subordinate to the higher priorities addressed in steps 1 through 4.

Traumatic brain injury detection using electrophysiological methods

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.

Multiscale Lempel-Ziv complexity for EEG measures

OBJECTIVE

To demonstrate that the classical calculation of Lempel-Ziv complexity (LZC) has an important limitation when applied to EEGs with rapid rhythms, and to propose a multiscale approach that overcomes this limitation.

METHODS

We have evaluated, both with simulated and real EEGs, whether LZC calculation neglects functional characteristics of rapid EEG rhythms. In addition, we have proposed a procedure to obtain multiple binarization sequences that yield a spectrum of LZC, and we have explored whether complexity would be better captured using this computation.

RESULTS

In our simulated signals, classical LZC did not capture modulations of a rapid component when a slower component of more amplitude was included in the signal. In real EEGs from healthy participants with eyes closed and eyes open, classical LZC calculation failed to show any difference between these two conditions. However, a multiscale LZC showed that complexity was lower for eyes closed than for eyes open conditions.

CONCLUSIONS

As hypothesized, our new approximation captures the complexity of series with fast components masked by slower rhythms.

SIGNIFICANCE

The method we introduce significantly improves LZC calculation, and it allows a better characterization of complexity of EEG signals.

BNCI systems as a potential assistive technology: ethical issues and participatory research in the BrainAble project

PURPOSE

This paper highlights aspects related to current research and thinking about ethical issues in relation to Brain Computer Interface (BCI) and Brain-Neuronal Computer Interfaces (BNCI) research through the experience of one particular project, BrainAble, which is exploring and developing the potential of these technologies to enable people with complex disabilities to control computers. It describes how ethical practice has been developed both within the multidisciplinary research team and with participants.

RESULTS

The paper presents findings in which participants shared their views of the project prototypes, of the potential of BCI/BNCI systems as an assistive technology, and of their other possible applications. This draws attention to the importance of ethical practice in projects where high expectations of technologies, and representations of "ideal types" of disabled users may reinforce stereotypes or drown out participant "voices".

CONCLUSIONS

Ethical frameworks for research and development in emergent areas such as BCI/BNCI systems should be based on broad notions of a "duty of care" while being sufficiently flexible that researchers can adapt project procedures according to participant needs. They need to be frequently revisited, not only in the light of experience, but also to ensure they reflect new research findings and ever more complex and powerful technologies.

Statistical Issues in TBI Clinical Studies

The identification and longitudinal assessment of traumatic brain injury presents several challenges. Because these injuries can have subtle effects, efforts to find quantitative physiological measures that can be used to characterize traumatic brain injury are receiving increased attention. The results of this research must be considered with care. Six reasons for cautious assessment are outlined in this paper. None of the issues raised here are new. They are standard elements in the technical literature that describes the mathematical analysis of clinical data. The purpose of this paper is to draw attention to these issues because they need to be considered when clinicians evaluate the usefulness of this research. In some instances these points are demonstrated by simulation studies of diagnostic processes. We take as an additional objective the explicit presentation of the mathematical methods used to reach these conclusions. This material is in the appendices. The following points are made: (1) A statistically significant separation of a clinical population from a control population does not ensure a successful diagnostic procedure. (2) Adding more variables to a diagnostic discrimination can, in some instances, actually reduce classification accuracy. (3) A high sensitivity and specificity in a TBI versus control population classification does not ensure diagnostic successes when the method is applied in a more general neuropsychiatric population. (4) Evaluation of treatment effectiveness must recognize that high variability is a pronounced characteristic of an injured central nervous system and that results can be confounded by either disease progression or spontaneous recovery. A large pre-treatment versus post-treatment effect size does not, of itself, establish a successful treatment. (5) A procedure for discriminating between treatment responders and non-responders requires, minimally, a two phase investigation. This procedure must include a mechanism to discriminate between treatment responders, placebo responders, and spontaneous recovery. (6) A search for prodromes of neuropsychiatric disorders following traumatic brain injury can be implemented with these procedures.

A review of EEG-based brain-computer interfaces as access pathways for individuals with severe disabilities

Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals. Considering the many differences in body functions and usage scenarios between individuals with disabilities and able-bodied individuals, involvement of the target population in BCI evaluation is necessary. In this review, 39 studies reporting EEG-oriented BCI assessment by individuals with disabilities were identified in the past decade. With respect to participant populations, a need for assessing BCI performance for the pediatric population with severe disabilities was identified as an important future direction. Acquiring a reliable communication pathway during early stages of development is crucial in avoiding learned helplessness in pediatric-onset disabilities. With respect to evaluation, augmenting traditional measures of system performance with those relating to contextual factors was recommended for realizing user-centered designs appropriate for integration in real-life. Considering indicators of user state and developing more effective training paradigms are recommended for future studies of BCI involving individuals with disabilities.

Behavioral deficits and axonal injury persistence after rotational head injury are direction dependent

Pigs continue to grow in importance as a tool in neuroscience. However, behavioral tests that have been validated in the rodent model do not translate well to pigs because of their very different responses to behavioral stimuli. We refined metrics for assessing porcine open field behavior to detect a wide spectrum of clinically relevant behaviors in the piglet post-traumatic brain injury (TBI). Female neonatal piglets underwent a rapid non-impact head rotation in the sagittal plane (n=8 evaluable) or were instrumented shams (n=7 evaluable). Open field testing was conducted 1 day prior to injury (day -1) in order to establish an individual baseline for analysis, and at days +1 and +4 after injury. Animals were then killed on day +6 after injury for neuropathological assessment of axonal injury. Injured piglets were less interested in interacting with environmental stimuli and had a lower activity level than did shams. These data were compared with previously published data for axial rotational injuries in neonatal piglets. Acute behavioral outcomes post-TBI showed a dependence on the rotational plane of the brain injury, with animals with sagittal injuries demonstrating a greater level of inactivity and less random usage of the open field space than those with axial injuries. The persistence of axonal injury is also dependent on the rotational plane, with sagittal rotations causing more prolonged injuries than axial rotations. These results are consistent with animal studies, finite element models, and studies of concussions in football, which have all demonstrated differences in injury severity depending upon the direction of head impact rotation.

Characterization of early partial seizure onset: frequency, complexity and entropy

OBJECTIVE

A clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed.

METHODS

Eighteen different measures including power in frequency bands up to 300 Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel-Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset. Intracranial recordings from 45 patients with mesial temporal, neocortical temporal and neocortical extratemporal seizure foci were included (331 partial seizures).

RESULTS

GAD, Lempel-Ziv complexity, HFD, high frequency activity, and sample entropy were the most reliable measures to assess early seizure onset.

CONCLUSIONS

Increases in complexity and occurrence of high-frequency components appear to be commonly associated with early stages of partial seizure evolution from all regions. The type of measure (frequency-based, complexity or entropy) does not predict the efficiency of the method to detect seizure onset.

SIGNIFICANCE

Differences between measures such as GAD and HFD highlight the multimodal nature of partial seizure onsets. Improved methods for early seizure detection may be achieved from a better understanding of these underlying dynamics.

Vasomotion becomes less random as diabetes progresses in monkeys

Please cite this paper as: Tigno, Hansen, Nawang, Shamekh, and Albano (2011). Vasomotion Becomes Less Random as Diabetes Progresses in Monkeys. Microcirculation 18(6), 429-439.

ABSTRACT

OBJECTIVE

  Changes in vasomotion may precede other global indices of autonomic dysfunction that track the onset and progression of diabetes. Recently, we showed that baseline spectral properties of vasomotion can discriminate among N, PreDM, and T2DM nonhuman primates. In this study, our aims were to: (i) determine the time dependence and complexity of the spectral properties of vasomotion in three metabolic groups of monkeys; (ii) examine the effects of heat-provoked vasodilatation on the power spectrum; and (iii) compare the effects of exogenous insulin on the vasomotion.

MATERIALS AND METHODS

  Laser Doppler flow rates were measured from the foot in 9 N, 11 PreDM, and 7 T2DM monkeys. Baseline flow was measured at 34°C, and under heat stimulation at 44°C. Euglycemic-hyperinsulinemic clamps were performed to produce acute hyperinsulinemia. The Lempel-Ziv complexity, prediction error, and covariance complexity of five-dimensional embeddings were calculated as measures of randomness.

RESULTS AND CONCLUSIONS

  With progression of diabetes, measures of randomness of the vasomotion progressively decreased, suggesting a progressive loss of the homeostatic capacity of the peripheral circulation to respond to environmental changes. Power spectral density among T2DM animals resided mostly in the 0- to 1.45-Hz range, which excluded the cardiac component, suggesting that with progression of the disease, regulation of flow shifts toward local rather than central (autonomic) mechanisms. Heating increased all components of the spectral power in all groups. In N, insulin increased the vasomotion contributed by endothelial, neurogenic, vascular myogenic, and respiratory processes, but diminished that due to heart rate. In contrast, in T2DM, insulin failed to stimulate the vascular myogenic and respiratory activities, but increased the neural/endothelial and heart rate components. Interestingly, acute hyperinsulinemia resulted in no significant vasomotion changes in the chronically hyperinsulinemic PreDM, suggesting yet another form of "insulin resistance" during this stage of the disease.

Communication patterns in a psychotherapy following traumatic brain injury: a quantitative case study based on symbolic dynamics

BACKGROUND

The role of psychotherapy in the treatment of traumatic brain injury is receiving increased attention. The evaluation of psychotherapy with these patients has been conducted largely in the absence of quantitative data concerning the therapy itself. Quantitative methods for characterizing the sequence-sensitive structure of patient-therapist communication are now being developed with the objective of improving the effectiveness of psychotherapy following traumatic brain injury.

METHODS

The content of three therapy session transcripts (sessions were separated by four months) obtained from a patient with a history of several motor vehicle accidents who was receiving dialectical behavior therapy was scored and analyzed using methods derived from the mathematical theory of symbolic dynamics.

RESULTS

The analysis of symbol frequencies was largely uninformative. When repeated triples were examined a marked pattern of change in content was observed over the three sessions. The context free grammar complexity and the Lempel-Ziv complexity were calculated for each therapy session. For both measures, the rate of complexity generation, expressed as bits per minute, increased longitudinally during the course of therapy. The between-session increases in complexity generation rates are consistent with calculations of mutual information. Taken together these results indicate that there was a quantifiable increase in the variability of patient-therapist verbal behavior during the course of therapy. Comparison of complexity values against values obtained from equiprobable random surrogates established the presence of a nonrandom structure in patient-therapist dialog (P = .002).

CONCLUSIONS

While recognizing that only limited conclusions can be based on a case history, it can be noted that these quantitative observations are consistent with qualitative clinical observations of increases in the flexibility of discourse during therapy. These procedures can be of particular value in the examination of therapies following traumatic brain injury because, in some presentations, these therapies are complicated by deficits that result in subtle distortions of language that produce significant post-injury social impairment. Independently of the mathematical analysis applied to the investigation of therapy-generated symbol sequences, our experience suggests that the procedures presented here are of value in training therapists.

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.

Quantitative characterization of animal behavior following blast exposure

The simplest approach to quantifying animal behavior begins by identifying a list of discrete behaviors and observing the animal's behavior at regular intervals for a specified period of time. The behavioral distribution (the fraction of observations corresponding to each behavior) is then determined. This is an incomplete characterization of behavior, and in some instances, mild injury is not reflected by statistically significant changes in the distribution even though a human observer can confidently and correctly assert that the animal is not behaving normally. In these circumstances, an examination of the sequential structure of the animal's behavior may, however, show significant alteration. This contribution describes procedures derived from symbolic dynamics for quantifying the sequential structure of animal behavior. Normalization procedures for complexity estimates are presented, and the limitations of complexity measures are discussed.

Reliability of quantitative EEG features

OBJECTIVE

To investigate the reliability of several well-known quantitative EEG (qEEG) features in the elderly in the resting, eyes closed condition and study the effects of epoch length and channel derivations on reliability.

METHODS

Fifteen healthy adults, over 50 years of age, underwent 10 EEG recordings over a 2-month period. Various qEEG features derived from power spectral, coherence, entropy and complexity analysis of the EEG were computed. Reliability was quantified using an intraclass correlation coefficient.

RESULTS

The highest reliability was obtained with the average montage, reliability increased with epoch length up to 40s, longer epochs gave only marginal improvement. The reliability of the qEEG features was highest for power spectral parameters, followed by regularity measures based on entropy and complexity, coherence being least reliable.

CONCLUSIONS

Montage and epoch length had considerable effects on reliability. Several apparently unrelated regularity measures had similar stability. Reliability of coherence measures was strongly dependent on channel location and frequency bands.

SIGNIFICANCE

The reliability of regularity measures has until now received limited attention. Low reliability of coherence measures in general may limit their usefulness in the clinical setting.

MAP-based kinetic analysis for voxel-by-voxel compartment model estimation: Detailed imaging of the cerebral glucose metabolism using FDG

We propose a novel algorithm for voxel-by-voxel compartment model analysis based on a maximum a posteriori (MAP) algorithm. Voxel-by-voxel compartment model analysis can derive functional images of living tissues, but it suffers from high noise statistics in voxel-based PET data and extended calculation times. We initially set up a feature space of the target radiopharmaceutical composed of a measured plasma time activity curve and a set of compartment model parameters, and measured the noise distribution of the PET data. The dynamic PET data were projected onto the feature space, and then clustered using the Mahalanobis distance. Our method was validated using simulation studies, and compared with ROI-based ordinary kinetic analysis for FDG. The parametric images exhibited an acceptable linear relation with the simulations and the ROI-based results, and the calculation time took about 10 min. We therefore concluded that our proposed MAP-based algorithm is practical.

Classification of the intention to generate a shoulder versus elbow torque by means of a time–frequency synthesized spatial patterns BCI algorithm

In this paper, we attempt to determine a subject's intention of generating torque at the shoulder or elbow, two neighboring joints, using scalp electroencephalogram signals from 163 electrodes for a brain-computer interface (BCI) application. To achieve this goal, we have applied a time-frequency synthesized spatial patterns (TFSP) BCI algorithm with a presorting procedure. Using this method, we were able to achieve an average recognition rate of 89% in four healthy subjects, which is comparable to the highest rates reported in the literature but now for tasks with much closer spatial representations on the motor cortex. This result demonstrates, for the first time, that the TFSP BCI method can be applied to separate intentions between generating static shoulder versus elbow torque. Furthermore, in this study, the potential application of this BCI algorithm for brain-injured patients was tested in one chronic hemiparetic stroke subject. A recognition rate of 76% was obtained, suggesting that this BCI method can provide a potential control signal for neural prostheses or other movement coordination improving devices for patients following brain injury.