Abstract-The

According to classical concepts of physiologic control, healthy systems are self-regulated to reduce variability and maintain physiologic constancy. Contrary to the predictions of homeostasis, however, the output of a wide variety of systems, such as the normal human heartbeat, fluctuates in a complex manner, even under resting conditions. Scaling techniques adapted from statistical physics reveal the presence of long-range, power-law correlations, as part of multifractal cascades operating over a wide range of time scales. These scaling properties suggest that the nonlinear regulatory systems are operating far from equilibrium, and that maintaining constancy is not the goal of physiologic control. In contrast, for subjects at high risk of sudden death (including those with heart failure), fractal organization, along with certain nonlinear interactions, breaks down. Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process. Similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease. Elucidating the fractal and nonlinear mechanisms involved in physiologic control and complex signaling networks is emerging as a major challenge in the postgenomic era.A hallmark of physiologic systems is their extraordinary complexity. The nonstationarity and nonlinearity of signals ( Fig. 1) generated by living organisms defy traditional mechanistic approaches based on homeostasis and conventional biostatistical methodologies. Recognition that physiologic time series contain ''hidden information'' has fueled growing interest in applying concepts and techniques from statistical physics, including chaos theory, to a wide range of biomedical problems from molecular to organismic levels (1, 2).This presentation describes one area of investigation that has engaged our collaborative attention, namely, fractal analysis of physiologic time series in health and disease. The discussion will focus primarily on certain features of the human heartbeat, one important example of complex physiologic fluctuations. The dynamics of another physiologic control system-human gait-is also briefly discussed. Recognizing that this topic represents only one selected aspect of the broad and rapidly expanding applications of complexity theory to biomedicine (Table 1), readers are referred to a number of useful reviews and references therein (1, 3-10).A motivating problem for our work is depicted in Fig. 1, which presents a dynamical self-test. Shown are 30-min heart rate time series from four subjects. Only one is from a healthy individual; the other three are from patients with life-threatening forms of heart disease. The problem is to identify the normal record. The (perhaps nonintuitive) answer to this ''test'' is given in the figure caption. Beyond its obvious diagnostic import, the problem of classifying temporal assays of integrated cardiac physiology has implications for understanding a...

Until recently, gait was generally viewed as a largely automated motor task, requiring minimal higher-level cognitive input. Increasing evidence, however, links alterations in executive function and attention to gait disturbances. This review discusses the role of executive function and attention in healthy walking and gait disorders while summarizing the relevant, recent literature. We describe the variety of gait disorders that may be associated with different aspects of executive function, and discuss the changes occurring in executive function as a result of aging and disease as well the potential impact of these changes on gait. The attentional demands of gait are often tested using dual tasking methodologies. Relevant studies in healthy adults and patients are presented, as are the possible mechanisms responsible for the deterioration of gait during dual tasking. Lastly, we suggest how assessments of executive function and attention could be applied in the clinical setting as part of the process of identifying and understanding gait disorders and fall risk. © 2007 Movement Disorder Society Key words: gait; executive function; attention; Parkinson's disease; Alzheimer's disease; aging; dual task; review article The relationship between higher-level cognitive function and gait disturbances has received considerable attention in recent years. Gait is no longer considered as merely an automated motor activity that utilizes minimal higher-level cognitive input. Instead, the multifaceted neuropsychological influences on walking and the interactions between the control of mobility and related behaviors are increasingly appreciated. This is manifest in part by an individual's awareness of a destination, the ability to appropriately control the limb movements that produce gait, and the ability to navigate within often complex environs to successfully reach the desired location. Studies on cognitive function and gait now include many areas of research, ranging from physiology and biomechanics to brain mapping, physics and neuropsychology. For example, imaging studies have demonstrated frontal and parietal activity during locomotion. 1,2 This review covers the importance and relevance of two specific cognitive functions, executive function (EF) and attention, to the performance of gait during normal walking, as well as in aging and in pathological conditions. We review the physiology underlying these cognitive processes, describe the clinical findings and consequences of these relationships and discuss the physiological mechanisms that are brought into play. Finally, we summarize the implications of these associations for the daily lives of individuals affected by impaired function of one or more or these elements and provide suggestions for applying these insights to augment the diagnosis of gait disorders in the clinic. This review article is based on a systematic literature search for reviews and trials reported in English in the electronic databases of Medline and Psychinfo up to April, 2007. Relevant s...

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Falls and freezing of gait are two "episodic" phenomena that are common in Parkinson's disease. Both symptoms are often incapacitating for affected patients, as the associated physical and psychosocial consequences have a great impact on the patients' quality of life, and survival is diminished. Furthermore, the resultant loss of independence and the treatment costs of injuries add substantially to the health care expenditures associated with Parkinson's disease. In this clinically oriented review, we summarise recent insights into falls and freezing of gait and highlight their similarities, differences, and links. Topics covered include the clinical presentation, recent ideas about the underlying pathophysiology, and the possibilities for treatment. A review of the literature and the current state-of-the-art suggests that clinicians should not feel deterred by the complex nature of falls and freezing of gait; a careful clinical approach may lead to an individually tailored treatment, which can offer at least partial relief for many affected patients.

Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. J. Appl. Physiol. 78(l): 349-358, 1995.-Complex fluctuations of unknown origin appear in the normal gait pattern. These fluctuations might be described as being 1) uncorrelated white noise, 2) short-range correlations, or 3) long-range correlations with power-law scaling. To test these possibilities, the stride interval of 10 healthy young men was measured as they walked for 9 min at their usual rate. From these time series, we calculated scaling indexes by using a modified random walk analysis and power spectral analysis. Both indexes indicated the presence of long-range self-similar correlations extending over hundreds of steps; the stride interval at any time depended on the stride interval at remote previous times, and this dependence decayed in a scale-free (fractallike) power-law fashion. These scaling indexes were significantly different from those obtained after random shuffling of the original time series, indicating the importance of the sequential ordering of the stride interval. We demonstrate that conventional models of gait generation fail to reproduce the observed scaling behavior and introduce a new type of central pattern generator model that successfully accounts for the experimentally observed long-range correlations. fluctuation analysis; locomotion; pattern generator; fractal computer modeling; central HUMAN GAIT is a Complex process. The locomotor system incorporates input from the cerebellum, the motor cortex, and the basal ganglia, as well as feedback from visual, vestibular, and proprioceptive sensors. Under healthy conditions, this multilevel control system produces a remarkably stable walking pattern; the kinetics, kinematics, and muscular activity of gait appear to remain relatively constant from one step to the next, even during unconstrained walking (12, 13,23, 24,40). Nevertheless, closer examination reveals fluctuations in the gait pattern, even under stationary conditions (7, 10, 23, 41, 42). We (11) and others (7,10,41,42) have observed considerable "noise" in one of the outputs of the locomotor system, the stride interval, defined as the time between the heel strike of one foot and the next heel strike of the same foot. A representative example of these complex fluctuations is shown in Fig. 1. One possible explanation for these stepto-step variations is that they simply represent uncorrelated (white) noise superimposed on a basically regular process. Alternatively, there could be short-range correlations in the stride interval such that the current value is influenced by only the most recent stride intervals, but over the long term, the fluctuations are random. A third, less intuitive, possibility is that the fluctuations in the stride interval exhibit long-range correlations, as seen in a wide class of scale-free phenomena (6, 14, 17, 28, 34, 38, 43). In this case, the stride interval at any instant would depend on the interval at relatively remote times, and this dependence woul...

We find that the successive increments in the cardiac beat-to-beat intervals of healthy subjects display scale-invariant, long-range anticorrelations (up to 10(4) heart beats). Furthermore, we find that the histogram for the heartbeat intervals increments is well described by a Lévy stable distribution. For a group of subjects with severe heart disease, we find that the distribution is unchanged, but the long-range correlations vanish. Therefore, the different scaling behavior in health and disease must relate to the underlying dynamics of the heartbeat.