Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease.

Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases.

Effective Connectivity During Rest and Music Listening: An EEG Study on Parkinson's Disease.

Music-based interventions seem to enhance motor, sensory and cognitive functions in Parkinson's disease (PD), but the underlying action mechanisms are still largely unknown. This electroencephalography (EEG) study aimed to investigate the effective connectivity patterns characterizing PD in the resting state and during music listening. EEG recordings were obtained from fourteen non-demented PD patients and 12 healthy controls, at rest and while listening to three music tracks. Theta- and alpha-band power spectral density and multivariate partial directed coherence were computed. Power and connectivity measures were compared between patients and controls in the four conditions and in music vs. rest. Compared to controls, patients showed enhanced theta-band power and slightly enhanced alpha-band power, but markedly reduced theta- and alpha-band interactions among EEG channels, especially concerning the information received by the right central channel. EEG power differences were partially reduced by music listening, which induced power increases in controls but not in patients. Connectivity differences were slightly compensated by music, whose effects largely depended on the track. In PD, music enhanced the frontotemporal inter-hemispheric communication. Our findings suggest that PD is characterized by enhanced activity but reduced information flow within the EEG network, being only partially normalized by music. Nevertheless, music capability to facilitate inter-hemispheric communication might underlie its beneficial effects on PD pathophysiology and should be further investigated.

Heart Rate Variability from Wearables: A Comparative Analysis Among Standard ECG, a Smart Shirt and a Wristband.

Heart Rate Variability (HRV) derived from standard one-lead electrocardiography (ECG) was compared with HRV computed by a commercial ECG shirt and with the inter-beat-intervals (IBI) measured by a research-grade photoplethysmographic (PPG) wristband. Signals from 8 subjects were recorded in three experimental phases: during sit; in upright position ("stand"); during controlled respiration. HRV and IBI from both the wearables were computed online (i.e. during the experiment) and stored for subsequent analyses, while the standard ECG was processed offline with state-of-the-art methods to obtain a clean reference HRV. Shirt and wristband signals accuracies were assessed, with respect to the reference HRV, through a between-phase and a beat-to-beat analyses. The former considered several time and frequency domain parameters; the latter was carried out through the Bland-Altman method. Time and frequency domain parameters computed from shirt HRV resulted very similar to the ones extracted from the reference HRV and generally more accurate than the parameters computed from wristband IBI. The Bland-Altman analysis showed that wristband IBI is significantly different from ECG-derived HRV, especially during "stand". Therefore, our results support the idea that some care should be paid in the interpretation of online PPG-derived IBI, while HRV measures online-derived from ECG-shirts seem to be more reliable in the analyzed conditions. The high number of missing beats also suggest that the design of wristband devices should be taken into account to reduce the rate of incorrect measurements, by maximizing sensor adhesion to the skin.

Characterization of the Asymmetry of the Cardiac and Sympathetic Arms of the Baroreflex From Spontaneous Variability During Incremental Head-Up Tilt.

Hysteresis of the baroreflex (BR) is the result of the different BR sensitivity (BRS) when arterial pressure (AP) rises or falls. This phenomenon has been poorly studied and almost exclusively examined by applying pharmacological challenges and static approaches disregarding causal relations. This study inspects the asymmetry of the cardiac BR (cBR) and vascular sympathetic BR (sBR) in physiological closed loop conditions from spontaneous fluctuations of physiological variables, namely heart period (HP) and systolic AP (SAP) leading to the estimation of cardiac BRS (cBRS) and muscle sympathetic nerve activity (MSNA) and diastolic AP (DAP) leading to the estimation of vascular sympathetic BRS (sBRS). The assessment was carried out in 12 young healthy subjects undergoing incremental head-up tilt with table inclination gradually increased from 0 to 60°. Two analytical methods were exploited and compared, namely the sequence (SEQ) and phase-rectified signal averaging (PRSA) methods. SEQ analysis is based on the detection of joint causal schemes representing the HP and MSNA burst rate delayed responses to spontaneous SAP and DAP modifications, respectively. PRSA analysis averages HP and MSNA burst rate patterns after aligning them according to the direction of SAP and DAP changes, respectively. Since cBRSs were similar when SAP went up or down, hysteresis of cBR was not detected. Conversely, hysteresis of sBR was evident with sBRS more negative when DAP was falling than rising. sBR hysteresis was no longer visible during sympathetic activation induced by the orthostatic challenge. These results were obtained via the SEQ method, while the PRSA technique appeared to be less powerful in describing the BR asymmetry due to the strong association between BRS estimates computed over positive and negative AP variations. This study suggests that cBR and sBR provide different information about the BR control, sBR exhibits more relevant non-linear features that are evident even during physiological changes of AP, and the SEQ method can be fruitfully exploited to characterize the BR hysteresis with promising applications to BR branches different from cBR and sBR.

Investigating the Optimal Baseline Positioning to Maximize Cognitive Experimental Outcome.

In emotional and cognitive research, the baseline is commonly used for standardization purposes in order to have a reference for the identification of the activation. Since no previous studies have investigated which moment of the experiment could be considered optimal for baseline evaluation, we designed an experimental protocol to analyze which time interval could be considered more effective in highlighting differences between the baseline state and the cognitive effort exhibited during tasks (specifically, reaction and working memory tasks). Several indexes were extracted from EEG signals during the visualization of the considered baseline stimuli and the execution of tasks. From our results, as regards to the considered Global Field Power (GFP) indexes (Attention and Memorization indexes), the last baseline stimulus seems to be the best one to highlight the difference in cognitive workload between the individual baseline condition and the two cognitive tasks. Instead, in terms of Engagement index (EI), the difference between Reaction Task (RT) and the individual baseline condition seems to be best highlighted by the relaxing video right after performed tasks. In conclusion, the best baseline position to maximize the differences in cognitive workload may vary among the considered indexes because of confounding effects and individual differences, but further analyses are required to validate this result.

Separating arterial pressure increases and decreases in assessing cardiac baroreflex sensitivity via sequence and bivariate phase-rectified signal averaging techniques.

Cardiac baroreflex (cBR) is activated by both arterial pressure (AP) increases and decreases. Sequence method, a widely utilized tool assessing cBR sensitivity (cBRS) from spontaneous heart period (HP) and systolic AP (SAP) variations, allows the separated computation of cBRS from positive and negative SAP variations. The recently proposed phase-rectified signal averaging (PRSA) method has the same feature but it has been applied so far solely to positive SAP variations. We adapted the PRSA method to compute cBRS over negative SAP variations and we compared the results with those derived from sequence method over two protocols: (i) graded head-up tilt (HUT) at 15, 30, 45, 60, and 75° in 19 healthy subjects and (ii) general anesthesia induction in 118 patients undergoing coronary artery bypass graft surgery. Regardless of the sign of SAP changes and method, cBRS moved toward 0 during HUT. Only sequence method detected the cBRS decrease after general anesthesia induction. In both protocols, the correlation between the PRSA-based cBRSs derived from positive and negative SAP changes was higher than that obtained from analogous sequence-based cBRSs and correlation between equivalent cBRSs derived from different methods might be absent. We conclude that the two methods are not interchangeable in assessing cBRS. Graphical abstract Graphical representation of the baroreflex sensitivity (BRS) estimation procedures carried out using sequence (SEQ) and phase-rectified signal averaging (PRSA) techniques over spontaneous fluctuations of heart period (HP) and systolic arterial pressure (SAP). BRSSEQ and BRSPRSA was separately computed over positive (+) and negative (-) SAP variations.

Pool size ratio of the substantia nigra in Parkinson's disease derived from two different quantitative magnetization transfer approaches.

PURPOSE: We sought to measure quantitative magnetization transfer (qMT) properties of the substantia nigra pars compacta (SNc) in patients with Parkinson's disease (PD) and healthy controls (HCs) using a full qMT analysis and determine whether a rapid single-point measurement yields equivalent results for pool size ratio (PSR). METHODS: Sixteen different MT-prepared MRI scans were obtained at 3 T from 16 PD patients and eight HCs, along with B1, B0, and relaxation time maps. Maps of PSR, free and macromolecular pool transverse relaxation times ([Formula: see text], [Formula: see text]) and rate of MT exchange between pools (k mf ) were generated using a full qMT model. PSR maps were also generated using a single-point qMT model requiring just two MT-prepared images. qMT parameter values of the SNc, red nucleus, cerebral crus, and gray matter were compared between groups and methods. RESULTS: PSR of the SNc was the only qMT parameter to differ significantly between groups (p < 0.05). PSR measured via single-point analysis was less variable than with the full MT model, provided slightly better differentiation of PD patients from HCs (area under curve 0.77 vs. 0.75) with sensitivity of 0.75 and specificity of 0.87, and was better than transverse relaxation time in distinguishing PD patients from HCs (area under curve 0.71, sensitivity 0.87, and specificity 0.50). CONCLUSION: The increased PSR observed in the SNc of PD patients may provide a novel biomarker of PD, possibly associated with an increased macromolecular content. Single-point PSR mapping with reduced variability and shorter scan times relative to the full qMT model appears clinically feasible.

Hemorrhage Prediction Models in Surgical Intensive Care: Bedside Monitoring Data Adds Information to Lab Values.

Hemorrhage is a frequent complication in surgery patients; its identification and management have received increasing attention as a target for quality improvement in patient care in the Intensive Care Unit (ICU). The purposes of this work were 1) to find an early detection model for hemorrhage by exploring the range of data mining methods that are currently available, and 2) to compare prediction models utilizing continuously measured physiological data from bedside monitors to those using commonly obtained laboratory tests. We studied 3766 patients admitted to the University of Virginia Health System Surgical Trauma Burn ICU. Hemorrhage was defined as three or more units of red blood cells transfused within 24 h without red blood cell transfusion in the preceding 24 h. 222 patients (5.9%) experienced a hemorrhage, and multivariate models based on vital signs and their trends showed good results (AUC = 76.1%). The hematocrit, not surprisingly, had excellent performance (AUC = 87.7%). Models that included both continuous monitoring and laboratory tests had the best performance (AUC = 92.2%). The results point to a combined strategy of continuous monitoring and intermittent lab tests as a reasonable clinical approach to the early detection of hemorrhage in the surgical ICU.

Contrast mechanisms associated with neuromelanin-MRI.

PURPOSE: To investigate the physical mechanisms associated with the contrast observed in neuromelanin MRI. METHODS: Phantoms having different concentrations of synthetic melanins with different degrees of iron loading were examined on a 3 Tesla scanner using relaxometry and quantitative magnetization transfer (MT). RESULTS: Concentration-dependent T1 and T2 shortening was most pronounced for the melanin pigment when combined with iron. Metal-free melanin had a negligible effect on the magnetization transfer spectra. On the contrary, the presence of iron-laden melanins resulted in a decreased magnetization transfer ratio. The presence of melanin or iron (or both) did not have a significant effect on the macromolecular content, represented by the pool size ratio. CONCLUSION: The primary mechanism underlying contrast in neuromelanin-MRI appears to be the T1 reduction associated with melanin-iron complexes. The macromolecular content is not significantly influenced by the presence of melanin with or without iron, and thus the MT is not directly affected. However, as T1 plays a role in determining the MT-weighted signal, the magnetization transfer ratio is reduced in the presence of melanin-iron complexes. Magn Reson Med 78:1790-1800, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Neuroimaging brainstem circuitry supporting cardiovagal response to pain: a combined heart rate variability/ultrahigh-field (7 T) functional magnetic resonance imaging study.

Central autonomic control nuclei in the brainstem have been difficult to evaluate non-invasively in humans. We applied ultrahigh-field (7 T) functional magnetic resonance imaging (fMRI), and the improved spatial resolution it affords (1.2 mm isotropic), to evaluate putative brainstem nuclei that control and/or sense pain-evoked cardiovagal modulation (high-frequency heart rate variability (HF-HRV) instantaneously estimated through a point-process approach). The time-variant HF-HRV signal was used to guide the general linear model analysis of neuroimaging data. Sustained (6 min) pain stimulation reduced cardiovagal modulation, with the most prominent reduction evident in the first 2 min. Brainstem nuclei associated with pain-evoked HF-HRV reduction were previously implicated in both autonomic regulation and pain processing. Specifically, clusters consistent with the rostral ventromedial medulla, ventral nucleus reticularis (Rt)/nucleus ambiguus (NAmb) and pontine nuclei (Pn) were found when contrasting sustained pain versus rest. Analysis of the initial 2-min period identified Rt/NAmb and Pn, in addition to clusters consistent with the dorsal motor nucleus of the vagus/nucleus of the solitary tract and locus coeruleus. Combining high spatial resolution fMRI and high temporal resolution HF-HRV allowed for a non-invasive characterization of brainstem nuclei, suggesting that nociceptive afference induces pain-processing brainstem nuclei to function in concert with known premotor autonomic nuclei in order to affect the cardiovagal response to pain.

Investigation of the electrophysiological correlates of negative BOLD response during intermittent photic stimulation: An EEG-fMRI study.

Although the occurrence of concomitant positive BOLD responses (PBRs) and negative BOLD responses (NBRs) to visual stimuli is increasingly investigated in neuroscience, it still lacks a definite explanation. Multimodal imaging represents a powerful tool to study the determinants of negative BOLD responses: the integration of functional Magnetic Resonance Imaging (fMRI) and electroencephalographic (EEG) recordings is especially useful, since it can give information on the neurovascular coupling underlying this complex phenomenon. In the present study, the brain response to intermittent photic stimulation (IPS) was investigated in a group of healthy subjects using simultaneous EEG-fMRI, with the main objective to study the electrophysiological mechanisms associated with the intense NBRs elicited by IPS in extra-striate visual cortex. The EEG analysis showed that IPS induced a desynchronization of the basal rhythm, followed by the instauration of a novel rhythm driven by the visual stimulation. The most interesting results emerged from the EEG-informed fMRI analysis, which suggested a relationship between the neuronal rhythms at 10 and 12 Hz and the BOLD dynamics in extra-striate visual cortex. These findings support the hypothesis that NBRs to visual stimuli may be neuronal in origin rather than reflecting pure vascular phenomena. Hum Brain Mapp 37:2247-2262, 2016. © 2016 Wiley Periodicals, Inc.

Brain Circuitry Supporting Multi-Organ Autonomic Outflow in Response to Nausea.

While autonomic outflow is an important co-factor of nausea physiology, central control of this outflow is poorly understood. We evaluated sympathetic (skin conductance level) and cardiovagal (high-frequency heart rate variability) modulation, collected synchronously with functional MRI (fMRI) data during nauseogenic visual stimulation aimed to induce vection in susceptible individuals. Autonomic data guided analysis of neuroimaging data, using a stimulus-based (analysis windows set by visual stimulation protocol) and percept-based (windows set by subjects' ratings) approach. Increased sympathetic and decreased parasympathetic modulation was associated with robust and anti-correlated brain activity in response to nausea. Specifically, greater autonomic response was associated with reduced fMRI signal in brain regions such as the insula, suggesting an inhibitory relationship with premotor brainstem nuclei. Interestingly, some sympathetic/parasympathetic specificity was noted. Activity in default mode network and visual motion areas was anti-correlated with parasympathetic outflow at peak nausea. In contrast, lateral prefrontal cortical activity was anti-correlated with sympathetic outflow during recovery, soon after cessation of nauseogenic stimulation. These results suggest divergent central autonomic control for sympathetic and parasympathetic response to nausea. Autonomic outflow and the central autonomic network underlying ANS response to nausea may be an important determinant of overall nausea intensity and, ultimately, a potential therapeutic target.

Cardiac Multi-detector CT Segmentation Based on Multiscale Directional Edge Detector and 3D Level Set.

Extraction of the cardiac surfaces of interest from multi-detector computed tomographic (MDCT) data is a pre-requisite step for cardiac analysis, as well as for image guidance procedures. Most of the existing methods need manual corrections, which is time-consuming. We present a fully automatic segmentation technique for the extraction of the right ventricle, left ventricular endocardium and epicardium from MDCT images. The method consists in a 3D level set surface evolution approach coupled to a new stopping function based on a multiscale directional second derivative Gaussian filter, which is able to stop propagation precisely on the real boundary of the structures of interest. We validated the segmentation method on 18 MDCT volumes from healthy and pathologic subjects using manual segmentation performed by a team of expert radiologists as gold standard. Segmentation errors were assessed for each structure resulting in a surface-to-surface mean error below 0.5 mm and a percentage of surface distance with errors less than 1 mm above 80%. Moreover, in comparison to other segmentation approaches, already proposed in previous work, our method presented an improved accuracy (with surface distance errors less than 1 mm increased of 8-20% for all structures). The obtained results suggest that our approach is accurate and effective for the segmentation of ventricular cavities and myocardium from MDCT images.

Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT-BIH databases.

BACKGROUND: Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis. GOAL: Previously, we devised numerical algorithms for identification of normal sinus rhythm (NSR), AF, and SR with frequent ectopy using dynamical measures of heart rate. Here, we wished to validate them in the canonical MIT-BIH ECG databases. METHODS: We tested algorithms on the NSR, AF and arrhythmia databases. RESULTS: When the databases were combined, the positive predictive value of the new algorithms exceeded 95% for NSR and AF, and was 40% for SR with ectopy. Further, dynamical measures did not distinguish atrial from ventricular ectopy. Inspection of individual 24hour records showed good correlation of observed and predicted rhythms. CONCLUSION: Heart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone.

Heart rate dynamics distinguish among atrial fibrillation, normal sinus rhythm and sinus rhythm with frequent ectopy.

Atrial fibrillation (AF) is usually detected by inspection of the electrocardiogram waveform, a task made difficult when the signal is distorted by noise. The RR interval time series is more frequently available and accurate, yet linear and nonlinear time series analyses that detect highly varying and irregular AF are vulnerable to the common finding of frequent ectopy. We hypothesized that different nonlinear measures might capture characteristic features of AF, normal sinus rhythm (NSR), and sinus rhythm (SR) with frequent ectopy in ways that linear measures might not. To test this, we studied 2722 patients with 24 h ECG recordings in the University of Virginia Holter database. We found dynamical phenotypes for the three rhythm classifications. As expected, AF records had the highest variability and entropy, and NSR the lowest. SR with ectopy could be distinguished from AF, which had higher entropy, and from NSR, which had different fractal scaling, measured as higher detrended fluctuation analysis slope. With these dynamical phenotypes, we developed successful classification strategies, and the nonlinear measures improved on the use of mean and variability alone, even after adjusting for age. Final models using all variables had excellent performance, with positive predictive values for AF, NSR and SR with ectopy as high as 97, 98 and 90%, respectively. Since these classifiers can reliably detect rhythm changes utilizing segments as short as 10 min, we envision their application in noisy settings and in personal monitoring devices where only RR interval time series may be available.

Non-Linear Heart Rate Variability Indices in the Frequent Hemodialysis Network Trials of Chronic Hemodialysis Patients.

BACKGROUND: Non-linear heart rate variability (HRV) indices were hypothesized to correlate with cardiac function, fluid overload and physical performance in hemodialysis patients. METHODS: Twenty-four-hour Holter electrocardiograms were recorded in patients enrolled in the Frequent Hemodialysis Network (FHN) Daily Dialysis Trial. Correlations between non-linear HRV indices and left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume (LVEDV), extracellular volume (ECV)/total body water (TBW) ratio, the SF-36 Physical Health Composite (PHC) and Physical Functioning (PF) scores were tested. RESULTS: We studied 210 subjects (average age 49.8 ± 13.5 years, 62% men, 42% diabetics). In non-diabetic patients, multiscale entropy (MSE) slope sample entropy (SampEn) and approximate entropy (ApEn) correlated positively with LVEF, PF and PHC and inversely with LVEDV and ECV/TBW. Spectral power slope correlated positively with ECV/TBW (r = 0.27). Irregularity measures (MSE ApEn and MSE SampEn) correlated positively with LVEDV (r = 0.19 and 0.20). CONCLUSION: Non-linear HRV indices indicated an association between a deteriorated heart rate regulatory system and impaired cardiac function, fluid accumulation and poor physical condition.

Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society.

Following the publication of the Task Force document on heart rate variability (HRV) in 1996, a number of articles have been published to describe new HRV methodologies and their application in different physiological and clinical studies. This document presents a critical review of the new methods. A particular attention has been paid to methodologies that have not been reported in the 1996 standardization document but have been more recently tested in sufficiently sized populations. The following methods were considered: Long-range correlation and fractal analysis; Short-term complexity; Entropy and regularity; and Nonlinear dynamical systems and chaotic behaviour. For each of these methods, technical aspects, clinical achievements, and suggestions for clinical application were reviewed. While the novel approaches have contributed in the technical understanding of the signal character of HRV, their success in developing new clinical tools, such as those for the identification of high-risk patients, has been rather limited. Available results obtained in selected populations of patients by specialized laboratories are nevertheless of interest but new prospective studies are needed. The investigation of new parameters, descriptive of the complex regulation mechanisms of heart rate, has to be encouraged because not all information in the HRV signal is captured by traditional methods. The new technologies thus could provide after proper validation, additional physiological, and clinical meaning. Multidisciplinary dialogue and specialized courses in the combination of clinical cardiology and complex signal processing methods seem warranted for further advances in studies of cardiac oscillations and in the understanding normal and abnormal cardiac control processes.

The forgotten role of central volume in low frequency oscillations of heart rate variability.

The hypothesis that central volume plays a key role in the source of low frequency (LF) oscillations of heart rate variability (HRV) was tested in a population of end stage renal disease patients undergoing conventional hemodialysis (HD) treatment, and thus subject to large fluid shifts and sympathetic activation. Fluid overload (FO) in 58 chronic HD patients was assessed by whole body bioimpedance measurements before the midweek HD session. Heart Rate Variability (HRV) was measured using 24-hour Holter electrocardiogram recordings starting before the same HD treatment. Time domain and frequency domain analyses were performed on HRV signals. Patients were retrospectively classified in three groups according to tertiles of FO normalized to the extracellular water (FO/ECW%). These groups were also compared after stratification by diabetes mellitus. Patients with the low to medium hydration status before the treatment (i.e. 1st and 2nd FO/ECW% tertiles) showed a significant increase in LF power during last 30 min of HD compared to dialysis begin, while no significant change in LF power was seen in the third group (i.e. those with high pre-treatment hydration values). In conclusion, several mechanisms can generate LF oscillations in the cardiovascular system, including baroreflex feedback loops and central oscillators. However, the current results emphasize the role played by the central volume in determining the power of LF oscillations.