INTRODUCTION:Spirituality is a recognized component of the DoD Chairman of the Joint Chief of Staff's Total Force Fitness Framework. Although it is often considered akin to religiosity, the Total Force Fitness framework took care to consider components not centered on a theist perspective. Spirituality is considered an adaptive interpersonal characteristic for human flourishing and functioning. It is therefore commonly supposed that spirituality supports resilience, and that spirituality and resilience should be correlated. This study presents data investigating this hypothesis. MATERIALS AND METHODS:A cohort of 452 junior officers (81.46% [n = 334] male; 25.69 ± 4.67 years old) in the U.S. Marine Corps completed an online survey including spirituality (Special Operations Command Spiritual Fitness Scale, SFS), and resilience (Ego-Resiliency Scale, ER89) metrics. The SFS was constructed to capture the elements of spiritual fitness as outlined in the Chairman of the Joint Chiefs of Staff Instruction 3405.01. RESULTS:Four procedures for quantifying correlation were implemented. All four found a moderate positive correlation between elements of spirituality and resilience. CONCLUSION:The results supported the hypothesis that personal spirituality is related to resilience in military personnel. Replication and longitudinal design of these constructs and their complexities will bolster ongoing military resilience and spirituality programs.

The differential entropy of a continuous waveform is defined over the period spanned by the recording. The time-dependent information dynamics of the observed process are not accessible to this measure. We review here the construction and validation of approximate time-dependent measures of information dynamics.

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.

Multichannel EEGs were obtained from healthy participants in the eyes-closed no-task 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. If the model order of integrated synergy was determined with the Bayesian Information Criterion, this pattern was reversed, and in common with other measures, integrated synergy was greater in the eyes open condition. Eyes open versus eyes closed separation was quantified by calculation of the between-condition effect size. Lempel-Ziv complexity of the first principal component showed greater separation than the measures of integrated information. The performance of the integrated information measures investigated here when distinguishing between indisputably different physiological states encourages caution when advocating for their use as measures of consciousness.

We present a generically applicable four-step process for quantifying information movement in complex networks. (1) Construction of local entropy rate and specific entropy rate. Local entropy rate is a continuous, time-dependent measure that quantifies the information gained at time t on observing x(t) given the recent past. There is a statistically responsible procedure for specifying “recent”. Specific entropy rate is a related time-dependent locally determined measure that gives an estimate of uncertainty at time t. (2) Construct specific transfer entropy (i.e., a time-dependent generalization of epoch-determined transfer entropy) that gives a state- and time-resolved quantification of the predictive input of a candidate input system on a candidate output system. (3) Construct a time-dependent network adjacency matrix. Specific transfer entropy can be used to populate the adjacency matrix characterizing a network. In the case of multichannel EEG/MEG recordings, the nodes are electrodes, and specific transfer entropy quantifies information movement between electrodes. In this analysis, the adjacency matrix is real, time-dependent and asymmetric. Any of a large number of measures commonly used to characterize an adjacency matrix can be used. The result Λ (t) is a scalar function of time. (4) Identify hierarchical transition chronometries in Λ (t) . The simple directive “find transitions in Λ (t) ” is unacceptably naive. Dynamically meaningful transitions are timescale-dependent. In this analysis, Λ (t) is embedded and the structure of this embedded object is examined by quadrant scans of the corresponding recurrence diagram. A hierarchy of transitions can be identified by manipulating the embedding dimension. We note that Λ (t) can serve as the order parameter in phase transition experiments in which time is the tuning parameter.

Using healthy adult participants, seven measures of heart rate variability were obtained simultaneously from four devices in five behavioral conditions. Two devices were ECG-based and two utilized photoplethysmography. The 140 numerical values (measure, condition, device) are presented. The comparative operational reliability of the four devices was assessed, and it was found that the two ECG-base devices were more reliable than the photoplethysmographic devices. The interchangeability of devices was assessed by determining the between-device Limits of Agreement. Intraclass correlation coefficients were determined and used to calculate the standard error of measurement and the Minimal Detectable Difference. The Minimal Detectable Difference, MDD, quantifies the smallest statistically significant change in a measure and is therefore critical when HRV measures are used longitudinally to assess treatment response or disease progression.

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.

Attenuation in P300 amplitude has been characterized in a wide range of neurological and psychiatric disorders such as dementia, schizophrenia, and posttraumatic stress disorder (PTSD). However, it is unclear whether the attenuation observed in the averaged event-related potential (ERP) is due to the reduction of neural resources available for cognitive processing, the decreased consistency of cognitive resource allocation, or the increased instability of cognitive processing speed. In this study, we investigated this problem by estimating single-trial P300 amplitude and latency using a modified Woody filter and examined the relation between amplitudes and latencies from the single-trial level to the averaged ERP level. ERPs were recorded from 30 military service members returning from combat deployment at two time points separated by 6 or 12 months. A conventional visual oddball task was used to elicit P300. We observed that the extent of changes in the within-subject average P300 amplitude over time was significantly correlated with the amount of change in three single-trial measures: (1) the latency variance of the single-trial P300 (r = −0.440, p = 0.0102); (2) the percentage of P300-absent trials (r = −0.488, p = 0.005); and (3) the consistent variation of the single-trial amplitude (r = 0.571, p = 0.0022). These findings suggest that there are multiple underlying mechanisms on the single-trial level that contribute to the changes in amplitudes seen at the averaged ERP level. The changes between the first and second assessments were quantified with the intraclass correlation coefficient, the standard error of measurement and the minimal detectable difference. The unique population, the small sample size and the large fraction of participants lost to follow up precludes generalizations of these measures of change to other populations.

Information dynamics provides a broad set of measures for characterizing how a dynamical system stores, processes, and transmits information. While estimators for these measures are commonly used in applications, the statistical properties of these estimators for finite time series are not well understood. In particular, the precision of a given estimate is generally unknown. We develop confidence intervals for generic information-dynamic parameters using a bootstrap procedure. The bootstrap procedure uses an echo state network, a particular instance of a reservoir computer, as a simulator to generate bootstrap samples from a given time series. We perform a Monte Carlo analysis to investigate the performance of the bootstrap confidence intervals in terms of their coverage and expected lengths with two model systems and compare their performance to a simulator based on the random analog predictor. We find that our bootstrap procedure generates confidence intervals with nominal, or near nominal, coverage of the information-dynamic measures, with smaller expected length than the random analog predictor-based confidence intervals. Finally, we demonstrate the applicability of the confidence intervals for characterizing the information dynamics of a time series of sunspot counts.

Military service members (SMs) returning from combat are at high risk of developing neuropsychiatric conditions such as posttraumatic stress disorder (PTSD) and major depression. Symptom dynamics following reintegration into civilian life may be magnified over time such that some SMs present with delayed onset and may not reach a diagnostic threshold for months to years. Monitoring the trajectory of mental health in the aftermath of combat trauma can therefore be particularly important in enhancing diagnosis. In this study, we investigated the possible utility of the P300 event-related potential (ERP) as an objective marker for monitoring post-trauma mental health. SMs recently returned from a combat deployment were recruited to undergo a baseline assessment, with subsequent follow-up assessment at 6 or 12 months later. At each assessment, ERPs were recorded using a conventional visual oddball task and a set of psychological scores assessing PTSD, depression, and psychosocial functioning were obtained. We observed that the individuals with overall improved psychological scores at follow-up had increased P300 amplitude and shortened P300 latency, and the individuals with overall worsened psychological scores at follow-up had prolonged P300 latency. The degree of change in aggregate psychological score was significantly correlated with the magnitude of change in P300 amplitude (r = -0.72, p < 0.0001) and latency (r = 0.42, p = 0.0201). These findings suggest that the P300 may be utilized as a quantitative biomarker for tracking the changes of mental health longitudinally. It may offer clinicians an objective tool for the assessment of the dynamics of mental health following trauma, and perhaps also for monitoring recovery during treatment.

In neuropsychiatric practice some patients present pathological deformations of consciousness. An understanding of the physiological basis of consciousness is therefore a clinical as well as scientific and philosophical problem. We review four possible responses to this clinical requirement. (1) absolute dualism, (2) McGinn's model of cognitive closure, (3) a model based on the inadequacy of physics, and (4) Wilczek's metaphor of mind brain complementarily. One possible quantification of consciousness, the integrated information theory of consciousness, is considered, and its limitations and the difficulties associated with its implementation are outlined. A less ambitious alternative based on an extension of information dynamics which offers the possibility of global time-dependent characterizations of central nervous system (CNS) information dynamics is presented. We suggest that while an integration of information dynamics and network theory may fail to solve the matter consciousness problem, the investigation could produce technologies and understandings that are clinically useful.

The objective of this research project is the identification of a physiological prodrome of post-traumatic stress disorder (PTSD) that has a reliability that could justify preemptive treatment in the sub-syndromal state. Because abnormalities in event-related potentials (ERPs) have been observed in fully expressed PTSD, the possible utility of abnormal ERPs in predicting delayed-onset PTSD was investigated. ERPs were recorded from military service members recently returned from Iraq or Afghanistan who did not meet PTSD diagnostic criteria at the time of ERP acquisition. Participants (n = 65) were followed for up to 1 year, and 7.7% of the cohorts (n = 5) were PTSD-positive at follow-up. The initial analysis of the receiver operating characteristic (ROC) curve constructed using ERP metrics was encouraging. The average amplitude to target stimuli gave an area under the ROC curve of greater than 0.8. Classification based on the Youden index, which is determined from the ROC, gave positive results. Using average target amplitude at electrode Cz yielded Sensitivity = 0.80 and Specificity = 0.87. A more systematic statistical analysis of the ERP data indicated that the ROC results may simply represent a fortuitous consequence of small sample size. Predicted error rates based on the distribution of target ERP amplitudes approached those of random classification. A leave-one-out cross validation using a Gaussian likelihood classifier with Bayesian priors gave lower values of sensitivity and specificity. In contrast with the ROC results, the leave-one-out classification at Cz gave Sensitivity = 0.65 and Specificity = 0.60. A bootstrap calculation, again using the Gaussian likelihood classifier at Cz, gave Sensitivity = 0.59 and Specificity = 0.68. Two provisional conclusions can be offered. First, the results can only be considered preliminary due to the small sample size, and a much larger study will be required to assess definitively the utility of ERP prodromes of PTSD. Second, it may be necessary to combine ERPs with other biomarkers in a multivariate metric to produce a prodrome that can justify preemptive treatment.

The Integrated Neuropsychiatric Assessment System is a portable platform for cognitive neurodynamic research and for clinical evaluations. It provides simultaneous acquisition of heart rate variability, quantitative electroencephalography and event related potentials during a neuropsychological assessment. The analysis protocol includes measures derived from dynamical systems theory including analysis of causal networks, event-dependent interregional synchronization and small world modeling.

Correlations between ten-channel EEGs obtained from thirteen healthy adult participants were investigated. Signals were obtained in two behavioral states: eyes open no task and eyes closed no task. Four time domain measures were compared: Pearson product moment correlation, Spearman rank order correlation, Kendall rank order correlation and mutual information. The psychophysiological utility of each measure was assessed by determining its ability to discriminate between conditions. The sensitivity to epoch length was assessed by repeating calculations with 1, 2, 3, …, 8 s epochs. The robustness to noise was assessed by performing calculations with noise corrupted versions of the original signals (SNRs of 0, 5 and 10 dB). Three results were obtained in these calculations. First, mutual information effectively discriminated between states with less data. Pearson, Spearman and Kendall failed to discriminate between states with a 1 s epoch, while a statistically significant separation was obtained with mutual information. Second, at all epoch durations tested, the measure of between-state discrimination was greater for mutual information. Third, discrimination based on mutual information was more robust to noise. The limitations of this study are discussed. Further comparisons should be made with frequency domain measures, with measures constructed with embedded data and with the maximal information coefficient.

Alterations of functional central nervous system networks occur as the result of traumatic brain injury. Characterization of functional network structure is therefore an essential element in the initial evaluation of these patients and in the longitudinal assessment of the response to treatment. Several methods of dynamical analysis have been used to quantify CNS reorganization during recovery. They include measures of inter-regional synchronization, analysis of network geometries (small world models) and the identification of causal networks. This project began with the identification of causal networks as its objective. This form of analysis moves beyond correlation by quantifying the direction and magnitude of information flow. An immediate difficulty presented itself. Consider a simple network consisting of two channels, Channel A and Channel B. Suppose that from time 0 t to 1 t Channel A is a strong causal driver of Channel B, and suppose further that from time 1 t to 2 t Channel B drives Channel A. If a causal analysis is constructed over the temporal epoch 0 t to 2 t , a failure to detect any causal relationships will probably result. It follows that an identification of transition chronometry is the first step in causality analysis. There are several classes of mathematical technologies for identifying transitions. Here we consider methods based on the examination of embedded data.

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.

Psychophysiological investigations of traumatic brain injury (TBI) are being conducted for several reasons, including the objective of learning more about the underlying physiological mechanisms of the pathological processes that can be initiated by a head injury. Additional goals include the development of objective physiologically based measures that can be used to monitor the response to treatment and to identify minimally symptomatic individuals who are at risk of delayed-onset neuropsychiatric disorders following injury. Research programs studying TBI search for relationships between psychophysiological measures, particularly ERP (event-related potential) component properties (e.g., timing, amplitude, scalp distribution), and a participant's clinical condition. Moreover, the complex relationships between brain injury and psychiatric disorders are receiving increased research attention, and ERP technologies are making contributions to this effort. This review has two objectives supporting such research efforts. The first is to review evidence indicating that TBI is a significant risk factor for post-injury neuropsychiatric disorders. The second objective is to introduce ERP researchers who are not familiar with neuropsychiatric assessment to the instruments that are available for characterizing TBI, post-concussion syndrome, and psychiatric disorders. Specific recommendations within this very large literature are made. We have proceeded on the assumption that, as is typically the case in an ERP laboratory, the investigators are not clinically qualified and that they will not have access to participant medical records.

We investigate the pairwise mutual information and transfer entropy of ten-channel, free-running electroencephalographs measured from thirteen subjects under two behavioral conditions: eyes open resting and eyes closed resting. Mutual information measures nonlinear correlations; transfer entropy determines the directionality of information transfer. For all channel pairs, mutual information is generally lower with eyes open compared to eyes closed indicating that EEG signals at different scalp sites become more dissimilar as the visual system is engaged. On the other hand, transfer entropy increases on average by almost two-fold when the eyes are opened. The largest one-way transfer entropies are to and from the Oz site consistent with the involvement of the occipital lobe in vision. The largest net transfer entropies are from F3 and F4 to almost all the other scalp sites.

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.

We investigate whether causal relationships in coupled Van der Pol oscillators can be determined. Using the instantaneous phase of computer generated time series, which are unidirectionally and time dependently coupled, the technique of lagged Mutual information is tested to determine its usefulness in detecting information transmission. If successful, it could assist in improving our understanding of the role of information transmission in organizing CNS activity.