Detrended fluctuation analysis of intracranial pressure predicts outcome following traumatic brain injury

Fractal complexity of daily physical activity and cognitive function in a midlife cohort

High stability of fluctuation in physiological patterns across fixed time periods suggest healthy fractal complexity, while greater randomness in fluctuation patterns may indicate underlying disease processes. The importance of fractal stability in mid-life remains unexplored. We quantified fractal regulation patterns in 24-h accelerometer data and examined associations with cognitive function in midlife. Data from 5097 individuals (aged 46) from the 1970 British Cohort Study were analyzed. Participants wore thigh-mounted accelerometers for seven days and completed cognitive tests (verbal fluency, memory, processing speed; derived composite z-score). Detrended fluctuation analysis (DFA) was used to examine temporal correlations of acceleration magnitude across 25 time scales (range: 1 min-10 h). Linear regression examined associations between DFA scaling exponents (DFAe) and each standardised cognitive outcome. DFAe was normally distributed (mean ± SD: 0.90 ± 0.06; range: 0.72-1.25). In males, a 0.10 increase in DFAe was associated with a 0.30 (95% Confidence Interval: 0.14, 0.47) increase in composite cognitive z-score in unadjusted models; associations were strongest for verbal fluency (0.10 [0.04, 0.16]). Associations remained in fully-adjusted models for verbal fluency only (0.06 [0.00, 0.12]). There was no association between DFA and cognition in females. Greater fractal stability in men was associated with better cognitive function. This could indicate mechanisms through which fractal complexity may scale up to and contribute to cognitive clinical endpoints.

Increased brain microvascular hemoglobin concentrations in children with cerebral malaria

Brain swelling is associated with death from cerebral malaria, but it is unclear whether brain swelling is caused by cerebral edema or vascular congestion-two pathological conditions with distinct effects on tissue hemoglobin concentrations. We used near-infrared spectroscopy (NIRS) to noninvasively study cerebral microvascular hemoglobin concentrations in 46 Malawian children with cerebral malaria. Cerebral malaria was defined by the presence of the malaria parasite Plasmodium falciparum on a blood smear, a Blantyre coma score of 2 or less, and retinopathy. Children with uncomplicated malaria (n = 33) and healthy children (n = 29) were enrolled as comparators. Cerebral microvascular hemoglobin concentrations were higher among children with cerebral malaria compared with those with uncomplicated malaria [median (25th, 75th): 145.2 (95.2, 190.0) μM versus 82.9 (65.7, 105.4) μM, P = 0.008]. Cerebral microvascular hemoglobin concentrations correlated with brain swelling score determined by MRI (r = 0.37, P = 0.03). Fluctuations in cerebral microvascular hemoglobin concentrations over a 30-min time period were characterized using detrended fluctuation analysis (DFA). DFA determined self-similarity of the cerebral microvascular hemoglobin concentration signal to be lower among children with cerebral malaria compared with those with uncomplicated malaria [0.63 (0.54, 0.70) versus 0.91 (0.82, 0.94), P < 0.0001]. The lower self-similarity of the hemoglobin concentration signal in children with cerebral malaria suggested impaired regulation of cerebral blood flow. The elevated cerebral tissue hemoglobin concentration and its correlation with brain swelling suggested that excess blood volume, potentially due to vascular congestion, may contribute to brain swelling in cerebral malaria.

Multifractal Characterization and Modeling of Blood Pressure Signals

In this paper, a multi-fractal analysis on a diastolic blood pressure signal is conducted. The signal is measured in a time span of circa one day through the multifractal detrended fluctuation analysis framework. The analysis is performed on asymptotic timescales where complex regulating mechanisms play a fundamental role in the blood pressure stability. Given a suitable frequency range and after removing non-stationarities, the blood pressure signal shows interesting scaling properties and a pronounced multifractality imputed to long-range correlations. Finally, a binomial multiplicative model is investigated showing how the analyzed signal can be described by a concise multifractal model with only two parameters.

Big data and predictive analytics in neurocritical care

PURPOSE OF REVIEW

To describe predictive data and workflow in the intensive care unit when managing neurologically ill patients.

RECENT FINDINGS

In the era of Big Data in medicine, intensive critical care units are data-rich environments. Neurocritical care adds another layer of data with advanced multimodal monitoring to prevent secondary brain injury from ischemia, tissue hypoxia, and a cascade of ongoing metabolic events. A step closer toward personalized medicine is the application of multimodal monitoring of cerebral hemodynamics, bran oxygenation, brain metabolism, and electrophysiologic indices, all of which have complex and dynamic interactions. These data are acquired and visualized using different tools and monitors facing multiple challenges toward the goal of the optimal decision support system. In this review, we highlight some of the predictive data used to diagnose, treat, and prognosticate the neurologically ill patients. We describe information management in neurocritical care units including data acquisition, wrangling, analysis, and visualization.

Nomogram Prediction of Short-Term Outcome After Intracerebral Hemorrhage

Background

The early symptoms of patients with elevated intracranial pressure (ICP) after intracerebral hemorrhage (ICH) are easily overlooked, which will result in missing the optimal opportunity for clinical intervention. However, it is difficult for ICH patients admitted to the neurology department to receive invasive ICP monitoring, although it is crucial for the early identification of neurologic deterioration (ND).

Objective

The aim of this study is to investigate the association between the changes of transcranial Doppler (TCD) variables and ND after onset and establish a nomogram for predicting the short-term outcome of ICH.

Methods

A total of 297 patients were recruited and their clinical characteristics and the changes of TCD variables were recorded. The independent prognostic factors for the ND after onset in the ICH patients were screened from multivariate Logistic regression analysis, which were served as inputs for the nomogram construction. Discrimination and calibration validations were performed to assess the performance of the nomogram [concordance index (C-index) for discrimination and Hosmer–Lemeshow (HL) test for calibration] and the decision curve analysis was applied to assess the clinical suitability.

Results

ΔaPI [defined as the change of pulsatility index (PI) between the 1st and 3rd day after onset for affected hemisphere] was independently associated with the ND after onset. Moreover, hematoma volume, presence of intraventricular hemorrhage, and Glasgow coma scale were also the independent prognostic factors of ND. The developed nomogram incorporating ΔaPI showed good discrimination (C-index: 0.916 after 1000 bootstrapping) and calibration (P=0.412, HL test) and yielded net benefits.

Conclusion

The nomogram incorporating ΔaPI might be useful in predicting the risk of ND within 14 days after onset, which might help identify patients in the neurology department in need of further care.

Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework

Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.

Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care

The critical care environment drives huge volumes of data, and clinicians are tasked with quickly processing this data and responding to it urgently. The neurocritical care environment increasingly involves EEG, multimodal intracranial monitoring, and complex imaging which preclude comprehensive human synthesis, and requires new concepts to integrate data into clinical care. By definition, Big Data is data that cannot be handled using traditional infrastructures and is characterized by the volume, variety, velocity, and variability of the data being produced. Big Data in the neurocritical care unit requires rethinking of data storage infrastructures and the development of tools and analytics to drive advancements in the field. Preprocessing, feature extraction, statistical inference, and analytic tools are required in order to achieve the primary goals of Big Data for clinical use: description, prediction, and prescription. Barriers to its use at bedside include a lack of infrastructure development within the healthcare industry, lack of standardization of data inputs, and ultimately existential and scientific concerns about the outputs that result from the use of tools such as artificial intelligence. However, as implied by the fundamental theorem of biomedical informatics, physicians remain central to the development and utility of Big Data to improve patient care.

Origins of 1/f noise in human music performance from short-range autocorrelations related to rhythmic structures

1/f fluctuations have been described in numerous physical and biological processes. This noise structure describes an inverse relationship between the intensity and frequency of events in a time series (for example reflected in power spectra), and is believed to indicate long-range dependence, whereby events at one time point influence events many observations later. 1/f has been identified in rhythmic behaviors, such as music, and is typically attributed to long-range correlations. However short-range dependence in musical performance is a well-established finding and past research has suggested that 1/f can arise from multiple continuing short-range processes. We tested this possibility using simulations and time-series modeling, complemented by traditional analyses using power spectra and detrended fluctuation analysis (as often adopted more recently). Our results show that 1/f-type fluctuations in musical contexts may be explained by short-range models involving multiple time lags, and the temporal ranges in which rhythmic hierarchies are expressed are apt to create these fluctuations through such short-range autocorrelations. We also analyzed gait, heartbeat, and resting-state EEG data, demonstrating the coexistence of multiple short-range processes and 1/f fluctuation in a variety of phenomena. This suggests that 1/f fluctuation might not indicate long-range correlations, and points to its likely origins in musical rhythm and related structures.

Non-linear Dynamical Analysis of Intraspinal Pressure Signal Predicts Outcome After Spinal Cord Injury

The injured spinal cord is a complex system influenced by many local and systemic factors that interact over many timescales. To help guide clinical management, we developed a technique that monitors intraspinal pressure from the injury site in patients with acute, severe traumatic spinal cord injuries. Here, we hypothesize that spinal cord injury alters the complex dynamics of the intraspinal pressure signal quantified by computing hourly the detrended fluctuation exponent alpha, multiscale entropy, and maximal Lyapunov exponent lambda. 49 patients with severe traumatic spinal cord injuries were monitored within 72 h of injury for 5 days on average to produce 5,941 h of intraspinal pressure data. We computed the spinal cord perfusion pressure as mean arterial pressure minus intraspinal pressure and the vascular pressure reactivity index as the running correlation coefficient between intraspinal pressure and arterial blood pressure. Mean patient follow-up was 17 months. We show that alpha values are greater than 0.5, which indicates that the intraspinal pressure signal is fractal. As alpha increases, intraspinal pressure decreases and spinal cord perfusion pressure increases with negative correlation between the vascular pressure reactivity index vs. alpha. Thus, secondary insults to the injured cord disrupt intraspinal pressure fractality. Our analysis shows that high intraspinal pressure, low spinal cord perfusion pressure, and impaired pressure reactivity strongly correlate with reduced multi-scale entropy, supporting the notion that secondary insults to the injured cord cause de-complexification of the intraspinal pressure signal, which may render the cord less adaptable to external changes. Healthy physiological systems are characterized by edge of chaos dynamics. We found negative correlations between the percentage of hours with edge of chaos dynamics (−0.01 ≤ lambda ≤ 0.01) vs. high intraspinal pressure and vs. low spinal cord perfusion pressure; these findings suggest that secondary insults render the intraspinal pressure more regular or chaotic. In a multivariate logistic regression model, better neurological status on admission, higher intraspinal pressure multi-scale entropy and more frequent edge of chaos intraspinal pressure dynamics predict long-term functional improvement. We conclude that spinal cord injury is associated with marked changes in non-linear intraspinal pressure metrics that carry prognostic information.

Idiopathic Intracranial Hypertension in Monozygotic Female Twins: Intracranial Pressure Dynamics and Treatment Outcome

BACKGROUND

Familial cases of idiopathic intracranial hypertension (IIH) are exceedingly rare, and its occurrence in monozygotic twins has not been reported previously.

CASE DESCRIPTION

We report monozygotic female twins who developed IIH, one at age 25 years and the other at age 28 years. Continuous intracranial pressure (ICP) monitoring confirmed elevated ICP as measured initially by lumbar puncture. In both cases, successful treatment with resolution of papilledema and symptoms relief was achieved after ventriculoperitoneal shunting.

CONCLUSIONS

This report documents the first case of IIH in monozygotic twins and the associated changes in ICP dynamics. Interestingly, almost equivalent alterations in ICP dynamics were found in the 2 patients.

Influence of general anaesthesia on slow waves of intracranial pressure

OBJECTIVE

Slow vasogenic intracranial pressure (ICP) waves are spontaneous ICP oscillations with a low frequency bandwidth of 0.3-4 cycles/min (B-waves). B-waves reflect dynamic oscillations in cerebral blood volume associated with autoregulatory cerebral vasodilation and vasoconstriction. This study quantifies the effects of general anaesthesia (GA) on the magnitude of B-waves compared to natural sleep and conscious state.

MATERIALS AND METHODS

The magnitude of B-waves was assessed in 4 groups of 30 patients each with clinical indications for ICP monitoring. Normal pressure hydrocephalus patients undergoing Cerebrospinal Fluid (CSF) infusion studies in the conscious state (GROUP A) and under GA (GROUP B), and hydrocephalus patients undergoing overnight ICP monitoring during physiological sleep (GROUP C) were compared to deeply sedated traumatic brain injury (TBI) patients with well-controlled ICP during the first night of Intensive Care Unit (ICU) stay (GROUP D).

RESULTS

A total of 120 patients were included. During CSF infusion studies, the magnitude of slow waves was higher in conscious patients (

GROUP A

0.23+/-0.10 mm Hg) when compared to anaesthetised patients (

GROUP B

0.15+/-0.10 mm Hg; p = 0.011). Overnight magnitude of slow waves was higher in patients during natural sleep (GROUP C: 0.20+/-0.13 mm Hg) when compared to TBI patients under deep sedation (GROUP D: 0.11+/- 0.09 mm Hg; p = 0.002).

CONCLUSION

GA and deep sedation are associated with a reduced magnitude of B-waves. ICP monitoring carried out under GA is affected by iatrogenic suppression of slow vasogenic waves of ICP. Accounting for the effects of anaesthesia on vasogenic waves may prevent the misidentification of potential shunt-responders as non-responders.

Data collection and interpretation

Patient monitoring is routinely performed in all patients who receive neurocritical care. The combined use of monitors, including the neurologic examination, laboratory analysis, imaging studies, and physiological parameters, is common in a platform called multi-modality monitoring (MMM). However, the full potential of MMM is only beginning to be realized since for the most part, decision making historically has focused on individual aspects of physiology in a largely threshold-based manner. The use of MMM now is being facilitated by the evolution of bio-informatics in critical care including developing techniques to acquire, store, retrieve, and display integrated data and new analytic techniques for optimal clinical decision making. In this review, we will discuss the crucial initial steps toward data and information management, which in this emerging era of data-intensive science is already shifting concepts of care for acute brain injury and has the potential to both reshape how we do research and enhance cost-effective clinical care.

Multimodality monitoring: informatics, integration data display and analysis

The goal of multimodality neuromonitoring is to provide continuous, real-time assessment of brain physiology to prevent, detect, and attenuate secondary brain injury. Clinical informatics deals with biomedical data, information, and knowledge including their acquisition, storage, retrieval, and optimal use for clinical decision-making. An electronic literature search was conducted for English language articles describing the use of informatics in the intensive care unit setting from January 1990 to August 2013. A total of 64 studies were included in this review. Clinical informatics infrastructure should be adopted that enables a wide range of linear and nonlinear analytical methods be applied to patient data. Specific time epochs of clinical interest should be reviewable. Analysis strategies of monitor alarms may help address alarm fatigue. Ergonomic data display that present results from analyses with clinical information in a sensible uncomplicated manner improve clinical decision-making. Collecting and archiving the highest resolution physiologic and phenotypic data in a comprehensive open format data warehouse is a crucial first step toward information management and two-way translational research for multimodality monitoring. The infrastructure required is largely the same as that needed for telemedicine intensive care applications, which under the right circumstances improves care quality while reducing cost.

Complex Adaptive Behavior and Dexterous Action

Dexterous action, as conceptualized by Bernstein in his influential ecological analysis of human behavior, is revealed in the ability to flexibly generate behaviors that are adaptively tailored to the demands of the context in which they are embedded. Conceived as complex adaptive behavior, dexterity depends upon the qualities of robustness and degeneracy, and is supported by the functional complexity of the agent-environment system. Using Bernstein's and Gibson's ecological analyses of behavior situated in natural environments as conceptual touchstones, we consider the hypothesis that complex adaptive behavior capitalizes upon general principles of self-organization. Here, we outline a perspective in which the complex interactivity of nervous-system, body, and environment is revealed as an essential resource for adaptive behavior. From this perspective, we consider the implications for interpreting the functionality and dysfunctionality of human behavior. This paper demonstrates that, optimal variability, the topic of this special issue, is a logical consequence of interpreting the functionality of human behavior as complex adaptive behavior.

Membrane current series monitoring: essential reduction of data points to finite number of stable parameters

In traditional studies of changes in cell membrane potential or trans-membrane currents a large part of the recorded data presents "a pure noise." This noise results mainly from the random openings of membrane ionic channels. Different types of stationary or non-stationary noise analysis have been used in electrophysiological experiments for identification of channels kinetic states. But these methods have a limited power and often cannot answer to the main question of the experimental study: do external factors induce a significant change of channels kinetics? A new method suggested in the current study is based on the scaling properties of the beta-distribution function that allows reducing the series containing 200,000 and more data points to analysis of only 10-20 stable parameters. The following clusterization using the generalized Pearson correlation function allows taking into account the influence of an external factor and combine/separate different parameters of interest into a statistical cluster considering the influential parameter. This method which we call BRC (Beta distribution-Reduction-Clusterization) opens new possibilities in creation of a largely reduced database while extracting specific fingerprints of the long-term series. The BRC method was validated using patch clamp current recordings containing 250,000 data points obtained from the living cells and from open tip electrode. The numerical distinction between these two series in terms of the reduced parameters was obtained.

A new algorithm for quadratic sample entropy optimization for very short biomedical signals: application to blood pressure records

This paper describes a new method to optimize the computation of the quadratic sample entropy (QSE) metric. The objective is to enhance its segmentation capability between pathological and healthy subjects for short and unevenly sampled biomedical records, like those obtained using ambulatory blood pressure monitoring (ABPM). In ABPM, blood pressure is measured every 20-30 min during 24h while patients undergo normal daily activities. ABPM is indicated for a number of applications such as white-coat, suspected, borderline, or masked hypertension. Hypertension is a very important clinical issue that can lead to serious health implications, and therefore its identification and characterization is of paramount importance. Nonlinear processing of signals by means of entropy calculation algorithms has been used in many medical applications to distinguish among signal classes. However, most of these methods do not perform well if the records are not long enough and/or not uniformly sampled. That is the case for ABPM records. These signals are extremely short and scattered with outliers or missing/resampled data. This is why ABPM Blood pressure signal screening using nonlinear methods is a quite unexplored field. We propose an additional stage for the computation of QSE independently of its parameter r and the input signal length. This enabled us to apply a segmentation process to ABPM records successfully. The experimental dataset consisted of 61 blood pressure data records of control and pathological subjects with only 52 samples per time series. The entropy estimation values obtained led to the segmentation of the two groups, while other standard nonlinear methods failed.

A Review of Theoretical Perspectives in Cognitive Science on the Presence of 1/f Scaling in Coordinated Physiological and Cognitive Processes

Time series of human performances present fluctuations around a mean value. These fluctuations are typically considered as insignificant, and attributable to random noise. Over recent decades, it became clear that temporal fluctuations possess interesting properties, however, one of which the property of fractal 1/f scaling. 1/f scaling indicates that a measured process extends over a wide range of timescales, suggesting an assembly over multiple scales simultaneously. This paper reviews neurological, physiological, and cognitive studies that corroborate the claim that 1/f scaling is most clearly present in healthy, well-coordinated activities. Prominent hypotheses about the origins of 1/f scaling are confronted with these reviewed studies. It is concluded that 1/f scaling in living systems appears to reflect their genuine complex nature, rather than constituting a coincidental side-effect. The consequences of fractal dynamics extending from the small spatial and temporal scales (e.g., neurons) to the larger scales of human behavior and cognition, are vast, and impact the way in which relevant research questions may be approached. Rather than focusing on specialized isolable subsystems, using additive linear methodologies, nonlinear dynamics, more elegantly so, imply a complex systems methodology, thereby exploiting, rather than rejecting, mathematical concepts that enable describing large sets of natural phenomena.

Analysis of Intracranial Pressure

The monitoring of intracranial pressure (ICP) is an important tool in medicine for its ability to portray the brain’s compliance status. The bedside monitor displays the ICP waveform and intermittent mean values to guide physicians in the management of patients, particularly those having sustained a traumatic brain injury. Researchers in the fields of engineering and physics have investigated various mathematical analysis techniques applicable to the waveform in order to extract additional diagnostic and prognostic information, although they largely remain limited to research applications. The purpose of this review is to present the current techniques used to monitor and interpret ICP and explore the potential of using advanced mathematical techniques to provide information about system perturbations from states of homeostasis. We discuss the limits of each proposed technique and we propose that nonlinear analysis could be a reliable approach to describe ICP signals over time, with the fractal dimension as a potential predictive clinically meaningful biomarker. Our goal is to stimulate translational research that can move modern analysis of ICP using these techniques into widespread practical use, and to investigate to the clinical utility of a tool capable of simplifying multiple variables obtained from various sensors.

Does sample rate introduce an artifact in spectral analysis of continuous processes?

Spectral analysis is a widely used method to estimate 1/f^α noise in behavioral and physiological data series. The aim of this paper is to achieve a more solid appreciation for the effects of periodic sampling on the outcomes of spectral analysis. It is shown that spectral analysis is biased by the choice of sample rate because denser sampling comes with lower amplitude fluctuations at the highest frequencies. Here we introduce an analytical strategy that compensates for this effect by focusing on a fixed amount, rather than a fixed percentage of the lowest frequencies in a power spectrum. Using this strategy, estimates of the degree of 1/f^α noise become robust against sample rate conversion and more sensitive overall. Altogether, the present contribution may shed new light on known discrepancies in the psychological literature on 1/f^α noise, and may provide a means to achieve a more solid framework for 1/f^α noise in continuous processes.

Reduced complexity of intracranial pressure observed in short time series of intracranial hypertension following traumatic brain injury in adults

Physiological parameters, such as intracranial pressure (ICP), are regulated by interconnected feedback loops, resulting in a complex time course. According to the decomplexification theory, disease is characterised by a loss of feedback loops resulting in a reduced complexity of the time course of physiological parameters. We hypothesized that complexity of the ICP time series is decreased during periods of intracranial hypertension (IHT) following adult traumatic brain injury. In an observational retrospective cohort study, ICP was continuously monitored using intraparenchymally implanted probes and stored using ICM + -software. Periods of IHT (ICP > 25 mmHg for at least 1,024 s), were compared with preceding periods of intracranial normotension (ICP < 20 mmHg) and analysed at 1 s-intervals. ICP data (length = 1,024 s) were normalised (mean = 0, SD = 1) and complexity was estimated using the scaling exponent α (as derived from detrended fluctuation analysis), sample entropy (SampEn, m = 1, r = 0.2 × SD) and multiscale entropy. 344 episodes were analysed in 22 patients. During IHT (ICP = 31.7 ± 7.8 mmHg, mean ± SD), α was significantly elevated (α = 1.02 ± 0.22, p < 0.001) and SampEn significantly reduced (SampEn = 1.45 ± 0.46, p = 0.004) as compared to before IHT (ICP = 15.7 ± 3.2 mmHg, α = 0.81 ± 0.14, SampEn = 1.81 ± 0.24). In addition, MSE revealed a significantly (p < 0.05) decreased entropy at scaling factors ranging from 1 to 10. Both the increase in α as well as the decrease in SampEn and MSE indicate a loss of ICP complexity. Therefore following traumatic brain injury, periods of IHT seem to be characterised by a decreased complexity of the ICP waveform.

Complexity of intracranial pressure correlates with outcome after traumatic brain injury

This study applied multiscale entropy analysis to investigate the correlation between the complexity of intracranial pressure waveform and outcome after traumatic brain injury. Intracranial pressure and arterial blood pressure waveforms were low-pass filtered to remove the respiratory and pulse components and then processed using a multiscale entropy algorithm to produce a complexity index. We identified significant differences across groups classified by the Glasgow Outcome Scale in intracranial pressure, pressure-reactivity index and complexity index of intracranial pressure (P < 0.0001; P = 0.001; P < 0.0001, respectively). Outcome was dichotomized as survival/death and also as favourable/unfavourable. The complexity index of intracranial pressure achieved the strongest statistical significance (F = 28.7; P < 0.0001 and F = 17.21; P < 0.0001, respectively) and was identified as a significant independent predictor of mortality and favourable outcome in a multivariable logistic regression model (P < 0.0001). The results of this study suggest that complexity of intracranial pressure assessed by multiscale entropy was significantly associated with outcome in patients with brain injury.

An interaction-dominant perspective on reading fluency and dyslexia

The background noise of response times is often overlooked in scientific inquiries of cognitive performances. However, it is becoming widely acknowledged in psychology, medicine, physiology, physics, and beyond that temporal patterns of variability constitute a rich source of information. Here, we introduce two complexity measures (1/f scaling and recurrence quantification analysis) that employ background noise as metrics of reading fluency. These measures gauge the extent of interdependence across, rather than within, cognitive components. In this study, we investigated dyslexic and non-dyslexic word-naming performance in beginning readers and observed that these complexity metrics differentiate reliably between dyslexic and average response times and correlate strongly with the severity of the reading impairment. The direction of change in the introduced metrics suggests that developmental dyslexia resides from dynamical instabilities in the coordination among the many components necessary to read, which could explain why dyslexic readers score below average on so many distinct tasks and modalities.

A trade-off study revealing nested timescales of constraint

This study investigates human performance in a cyclic Fitts task at three different scales of observation, either in the presence (difficult condition) or in the absence (easy condition) of a speed–accuracy trade-off. At the fastest scale, the harmonicity of the back and forth movements, which reflects the dissipation of mechanical energy, was measured within the timeframe of single trials. At an intermediate scale, speed and accuracy measures were determined over a trial. The slowest scale pertains to the temporal structure of movement variability, which evolves over multiple trials. In the difficult condition, reliable correlations across each of the measures corroborated a coupling of nested scales of performance. Participants who predominantly emphasized the speed-side of the trade-off (despite the instruction to be both fast and accurate) produced more harmonic movements and clearer 1/f scaling in the produced movement time series, but were less accurate and produced more random variability in the produced movement amplitudes (vice versa for more accurate participants). This implied that speed–accuracy trade-off was accompanied by a trade-off between temporal and spatial streams of 1/f scaling, as confirmed by entropy measures. In the easy condition, however, no trade-offs nor couplings among scales of performance were observed. Together, these results suggest that 1/f scaling is more than just a byproduct of cognition. These findings rather support the claim that interaction-dominant dynamics constitute a coordinative basis for goal-directed behavior.