Chi-Sang Poon

Decrease of cardiac chaos in congestive heart failure

The electrical properties of the mammalian heart undergo many complex transitions in normal and diseased states. It has been proposed that the normal heartbeat may display complex nonlinear dynamics, including deterministic chaos,, and that such cardiac chaos may be a useful physiological marker for the diagnosis and management, of certain heart trouble. However, it is not clear whether the heartbeat series of healthy and diseased hearts are chaotic or stochastic, or whether cardiac chaos represents normal or abnormal behaviour. Here we have used a highly sensitive technique, which is robust to random noise, to detect chaos. We analysed the electrocardiograms from a group of healthy subjects and those with severe congestive heart failure (CHF), a clinical condition associated with a high risk of sudden death. The short-term variations of beat-to-beat interval exhibited strongly and consistently chaotic behaviour in all healthy subjects, but were frequently interrupted by periods of seemingly non-chaotic fluctuations in patients with CHF. Chaotic dynamics in the CHF data, even when discernible, exhibited a high degree of random variability over time, suggesting a weaker form of chaos. These findings suggest that cardiac chaos is prevalent in healthy heart, and a decrease in such chaos may be indicative of CHF.

Analysis of First-Derivative Based QRS Detection Algorithms

Accurate QRS detection is an important first step for the analysis of heart rate variability. Algorithms based on the differentiated ECG are computationally efficient and hence ideal for real-time analysis of large datasets. Here, we analyze traditional first-derivative based squaring function (Hamilton-Tompkins) and Hilbert transform-based methods for QRS detection and their modifications with improved detection thresholds. On a standard ECG dataset, the Hamilton-Tompkins algorithm had the highest detection accuracy (99.68% sensitivity, 99.63% positive predictivity) but also the largest time error. The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination. The high accuracy of the Hilbert transform-based method compared to detection with the second derivative of the ECG is ascribable to its inherently uniform magnitude spectrum. For all algorithms, detection errors occurred mainly in beats with decreased signal slope, such as wide arrhythmic beats or attenuated beats. For best performance, a combination of the squaring function and Hilbert transform-based algorithms can be applied such that differences in detection will point to abnormalities in the signal that can be further analyzed.

Titration of chaos with added noise

Deterministic chaos has been implicated in numerous natural and man-made complex phenomena ranging from quantum to astronomical scales and in disciplines as diverse as meteorology, physiology, ecology, and economics. However, the lack of a definitive test of chaos vs. random noise in experimental time series has led to considerable controversy in many fields. Here we propose a numerical titration procedure as a simple “litmus test” for highly sensitive, specific, and robust detection of chaos in short noisy data without the need for intensive surrogate data testing. We show that the controlled addition of white or colored noise to a signal with a preexisting noise floor results in a titration index that: (i) faithfully tracks the onset of deterministic chaos in all standard bifurcation routes to chaos; and (ii) gives a relative measure of chaos intensity. Such reliable detection and quantification of chaos under severe conditions of relatively low signal-to-noise ratio is of great interest, as it may open potential practical ways of identifying, forecasting, and controlling complex behaviors in a wide variety of physical, biomedical, and socioeconomic systems.

Habituation and desensitization of the Hering-Breuer reflex in rat

1 Many processes in mammalian and invertebrate central nervous systems exhibit habituation and/or sensitization of their responses to repetitive stimuli. Here, we studied the adaptive behaviours of the respiratory pattern generator in rat on repetitive vagal‐afferent stimulation and compared these behaviours obtained in vivo with the reported effects of such stimuli on synaptic transmission in the corresponding signal pathway in vitro. 2 Sustained (1 min) electrical pulsed stimulation of the vagus nerve elicited the classic Hering‐Breuer (HB) reflex slowing of the respiratory rhythm followed by a bi‐exponential recovery, and a post‐stimulus rebound (PR). The recovery from the HB reflex satisfied the classic criteria of habituation. 3 The fast component of the recovery and the PR were abolished by systemic administration of an NMDA receptor antagonist or electrolytic lesioning of the pontine Kölliker‐Fuse nucleus. The characteristics of the fast recovery and PR suggest a vagally induced desensitization of the NMDA receptor‐dependent pontine input to the respiratory pattern generator. 4 The slow component of recovery persisted after both experimental interventions and accounted for the habituation to the vagal input. The characteristics of the slow recovery in vivo were reminiscent of the reported synaptic accommodation in vitro in the medullary region where vagal afferents terminate. 5 The habituation of vagal input and desensitization of pontine input act in concert to offset the HB reflex. Such simultaneous habituation‐desensitization in parallel neural pathways with differing sensitivities to NMDA receptor activation represent a hitherto unknown pairing of dual non‐associative learning processes in the mammalian brain.

Chaotic signatures of heart rate variability and its power spectrum in health, aging and heart failure.

A paradox regarding the classic power spectral analysis of heart rate variability (HRV) is whether the characteristic high- (HF) and low-frequency (LF) spectral peaks represent stochastic or chaotic phenomena. Resolution of this fundamental issue is key to unraveling the mechanisms of HRV, which is critical to its proper use as a noninvasive marker for cardiac mortality risk assessment and stratification in congestive heart failure (CHF) and other cardiac dysfunctions. However, conventional techniques of nonlinear time series analysis generally lack sufficient sensitivity, specificity and robustness to discriminate chaos from random noise, much less quantify the chaos level. Here, we apply a ‘litmus test’ for heartbeat chaos based on a novel noise titration assay which affords a robust, specific, time-resolved and quantitative measure of the relative chaos level. Noise titration of running short-segment Holter tachograms from healthy subjects revealed circadian-dependent (or sleep/wake-dependent) heartbeat chaos that was linked to the HF component (respiratory sinus arrhythmia). The relative ‘HF chaos’ levels were similar in young and elderly subjects despite proportional age-related decreases in HF and LF power. In contrast, the near-regular heartbeat in CHF patients was primarily nonchaotic except punctuated by undetected ectopic beats and other abnormal beats, causing transient chaos. Such profound circadian-, age- and CHF-dependent changes in the chaotic and spectral characteristics of HRV were accompanied by little changes in approximate entropy, a measure of signal irregularity. The salient chaotic signatures of HRV in these subject groups reveal distinct autonomic, cardiac, respiratory and circadian/sleep-wake mechanisms that distinguish health and aging from CHF.

Chaotic dynamics of resting ventilatory flow in humans assessed through noise titration

The mammalian ventilatory behaviour exhibits nonlinear dynamics as reflected by certain nonlinearity or complexity indicators (e.g. correlation dimension, approximate entropy, Lyapunov exponents, etc.) but this is not sufficient to determine its possible chaotic nature. To address this, we applied the noise titration technique, previously shown to discern and quantify chaos in short and noisy time series, to ventilatory flow recordings obtained in quietly breathing normal humans. Nine subjects (8 men and 1 woman, 24-42 years) were studied during 15-min epochs of ventilatory steady-state (10.1+/-3.0 breaths/min, tidal volume 0.63+/-0.2 L). Noise titration applied to the unfiltered signals subsampled at 5 Hz detected nonlinearity in all cases (noise limit 20.2+/-12.5%). Noise limit values were weakly correlated to the correlation dimension and the largest Lyapunov exponent of the signals. This study shows that the noise titration approach evidences a chaotic dimension to the behavior of ventilatory flow over time in normal humans during tidal breathing.

Plasticity of cardiorespiratory neural processing: classification and computational functions

Neural plasticity, or malleability of neuronal structure and function, is an important attribute of the mammalian forebrain and is generally thought to be a kernel of biological intelligence. In this review, we examine some reported manifestations of neural plasticity in the cardiorespiratory system and classify them into four functional categories, integral; differential; memory; and statistical-type plasticity. At the cellular and systems level the myriad forms of cardiorespiratory plasticity display emergent and self-organization properties, use- and disuse-dependent and pairing-specific properties, short-term and long-term potentiation or depression, as well as redundancy in series or parallel structures, convergent pathways or backup and fail-safe surrogate pathways. At the behavioral level, the cardiorespiratory system demonstrates the capability of associative and nonassociative learning, classical and operant conditioning as well as short-term and long-term memory. The remarkable similarity and consistency of the various types of plasticity exhibited at all levels of organization suggest that neural plasticity is integral to cardiorespiratory control and may subserve important physiological functions.

Functional and structural models of pontine modulation of mechanoreceptor and chemoreceptor reflexes

The dorsolateral and ventrolateral pons (dl-pons, vl-pons) are critical brainstem structures mediating the plasticity of the Hering-Breuer mechanoreflex (HBR) and carotid chemoreflex (CCR). Review of anatomical evidence indicates that dl-pons and vl-pons are connected reciprocally with one another and with medullary nucleus tractus solitarius (NTS) and ventral respiratory group (VRG). With this structural map, functional models of HBR and CCR are proposed in which the respiratory rhythm is modulated by short-term depression (STD) or potentiation (STP) of corresponding primary NTS-VRG and auxiliary pons-VRG excitatory or inhibitory pathways. Behaviorally, STD and STP of respiratory reflexes are akin to non-associative learning such as habituation, sensitization or desensitization to afferent inputs. Computationally, the STD and STP effects amount to signal differentiation and integration in the time domain, or high-pass and low-pass filtering in the frequency domain, respectively. These functional and structural models of pontomedullary signal processing provide a novel conceptual framework that unifies a wealth of experimental observations regarding mechanoreceptor and chemoreceptor reflex control of breathing.

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.

Cytoarchitecture of Pneumotaxic Integration of Respiratory and Nonrespiratory Information in the Rat

The “pneumotaxic center” in the Kölliker-Fuse and medial parabrachial nuclei of dorsolateral pons (dl-pons) plays an important role in respiratory phase switching, modulation of respiratory reflex, and rhythmogenesis. Recent electrophysiological and neural tracing data implicate additional pneumotaxic nuclei in (and a broader role for) the dl-pons in integrating respiratory and nonrespiratory information. Here, we examined the cytoarchitecture of the greater pneumotaxic center and its integrating function by using combined extracellular recording and juxtacellular labeling of unit respiratory rhythmic neurons in dl-pons in urethane-anesthetized, vagotomized, paralyzed, and servo-ventilated adult Sprague Dawley rats. Perievent histogram analysis identified four major types of neuronal discharge patterns: inspiratory, expiratory (with three subdivisions), inspiratory–expiratory, and expiratory–inspiratory phase spanning, sometimes with mild tonic background activity. Most recorded neurons were localized in the Kölliker-Fuse and medial parabrachial nuclei, but some were also found in lateral parabrachial nucleus, intertrigeminal nucleus, principal trigeminal sensory nucleus, and supratrigeminal nucleus. The majority of labeled neurons had large and spatially extended dendritic trees that spanned several of these dl-pons subnuclei, often with terminal dendrites ending in the ventral spinocerebellar tract. The distal sections of the primary and higher-order dendrites exhibited rich varicosities, sometimes with dendritic spines. Axons of some labeled neurons were traced all the way to the ventrolateral pons (vl-pons). These findings extend and generalize the classical definition of the pneumotaxic center to include extensive somatic–axonal–dendritic integration of complex descending and ascending respiratory information as well as nociceptive and possibly musculoskeletal and trigeminal information in multiple dl-pons and vl-pons structures in the rat.