Heart rate recovery after exercise: relations to heart rate variability and complexity

Physical exercise is associated with parasympathetic withdrawal and increased sympathetic activity resulting in heart rate increase. The rate of post-exercise cardiodeceleration is used as an index of cardiac vagal reactivation. Analysis of heart rate variability (HRV) and complexity can provide useful information about autonomic control of the cardiovascular system. The aim of the present study was to ascertain the association between heart rate decrease after exercise and HRV parameters. Heart rate was monitored in 17 healthy male subjects (mean age: 20 years) during the pre-exercise phase (25 min supine, 5 min standing), during exercise (8 min of the step test with an ascending frequency corresponding to 70% of individual maximal power output) and during the recovery phase (30 min supine). HRV analysis in the time and frequency domains and evaluation of a newly developed complexity measure - sample entropy - were performed on selected segments of heart rate time series. During recovery, heart rate decreased gradually but did not attain pre-exercise values within 30 min after exercise. On the other hand, HRV gradually increased, but did not regain rest values during the study period. Heart rate complexity was slightly reduced after exercise and attained rest values after 30-min recovery. The rate of cardiodeceleration did not correlate with pre-exercise HRV parameters, but positively correlated with HRV measures and sample entropy obtained from the early phases of recovery. In conclusion, the cardiodeceleration rate is independent of HRV measures during the rest period but it is related to early post-exercise recovery HRV measures, confirming a parasympathetic contribution to this phase.

Topographic abnormality of slow cortical potentials in schizophrenia

A recent study from our laboratory has provided evidence for the generation of slow potentials occurring in anticipation to task-performance feedback stimuli, in multiple association cortical areas, consistently including two prefrontal areas. In the present study, we intended to determine whether these slow potentials would indicate some abnormality (topographic) in schizophrenic patients, and thus serve as an indication of abnormal association cortex activity. We recorded slow potentials while subjects performed a paired-associates memory task. A 123-channel EEG montage and common average reference were used for 20 unmedicated schizophrenic (mean duration of illness: 11.3 ± 9.2 years; mean number of previous hospitalizations: 1.2 ± 1.9) and 22 healthy control subjects during a visual paired-associates matching task. For the topographic analysis, we used a simple index of individual topographic deviation from normality, corrected for absolute potential intensities. Slow potentials were observed in all subjects. Control subjects showed a simple spatial pattern of voltage extrema (left central positive and right prefrontal negative), whereas schizophrenic patients presented a more complex, fragmented pattern. Topographic deviation was significantly different between groups (P < 0.001). The increased topographic complexity in schizophrenics could be visualized in grand averages computed across subjects. Increased topographic complexity could also be seen when grand averages were computed for subgroups of patients assembled either according to task-performance (high versus low) or by their scores on psychopathological scales. There was no significant correlation between topographic deviation and psychopathology scores. We conclude that the slow potential topographic abnormalities of schizophrenia indicate an abnormality in the configuration of large-scale electrical activity in association cortices.

Learning about brain physiology and complexity from the study of the epilepsies

The brain is a complex system, which produces emergent properties such as those associated with activity-dependent plasticity in processes of learning and memory. Therefore, understanding the integrated structures and functions of the brain is well beyond the scope of either superficial or extremely reductionistic approaches. Although a combination of zoom-in and zoom-out strategies is desirable when the brain is studied, constructing the appropriate interfaces to connect all levels of analysis is one of the most difficult challenges of contemporary neuroscience. Is it possible to build appropriate models of brain function and dysfunctions with computational tools? Among the best-known brain dysfunctions, epilepsies are neurological syndromes that reach a variety of networks, from widespread anatomical brain circuits to local molecular environments. One logical question would be: are those complex brain networks always producing maladaptive emergent properties compatible with epileptogenic substrates? The present review will deal with this question and will try to answer it by illustrating several points from the literature and from our laboratory data, with examples at the behavioral, electrophysiological, cellular and molecular levels. We conclude that, because the brain is a complex system compatible with the production of emergent properties, including plasticity, its functions should be approached using an integrated view. Concepts such as brain networks, graphics theory, neuroinformatics, and e-neuroscience are discussed as new transdisciplinary approaches dealing with the continuous growth of information about brain physiology and its dysfunctions. The epilepsies are discussed as neurobiological models of complex systems displaying maladaptive plasticity.

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.