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Statistical coding and decoding of heartbeat intervals.

Lucena F, Barros AK, Príncipe JC, Ohnishi N - PLoS ONE (2011)

Bottom Line: Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm.We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation.Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

View Article: PubMed Central - PubMed

Affiliation: Biological Information Engineering Laboratory, Nagoya University, Nagoya, Aichi, Japan. lucena@ohnishi.nagoya-u.ac.jp

ABSTRACT
The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

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Comparing cardiac response (black line) with filter response (magenta line).The responses are shown in units representing the standard deviation. (A) The sympathetic system response to a stimulus intensity chosen from a continuous signal that was drawn randomly from a Gaussian distribution in contrast to a decoding filter response (SNR = 14.05 dB). (B) The corresponding vagal nerve (PNS) response and its estimated response using a decoding filter (SNR = 12.62 dB). Besides the fast oscillations, the decoding filters yielded a response the tracks fairly well the observed (physiological) cardiac response.
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pone-0020227-g009: Comparing cardiac response (black line) with filter response (magenta line).The responses are shown in units representing the standard deviation. (A) The sympathetic system response to a stimulus intensity chosen from a continuous signal that was drawn randomly from a Gaussian distribution in contrast to a decoding filter response (SNR = 14.05 dB). (B) The corresponding vagal nerve (PNS) response and its estimated response using a decoding filter (SNR = 12.62 dB). Besides the fast oscillations, the decoding filters yielded a response the tracks fairly well the observed (physiological) cardiac response.

Mentions: where represents the estimated filter response and a scaling factor (see methods). We have to include the search for the best scaling factor because the ICA decomposition is blind to scaling. Using a dataset derived from rabbits in a time window (see methods), the decoding population overall response is smoother then the observed cardiac output. The estimated responses, for both sympathetic (Fig. 9A) and vagal (Fig. 9B) stimulation follow the expected heart response, but lack the fast oscillations. We also quantify the reliability of the decoding response by measuring the relationship between the cardiac response and the estimated noise in (3) using the signal-to-noise ratio, SNR(dB) , by sliding a time-window length of 270 seconds over 24 heart rate intervals. The SNR varies from to dB ( Fig. 10), which correspond to approximately and percent of accuracy in psychophysics [48] (see methods).


Statistical coding and decoding of heartbeat intervals.

Lucena F, Barros AK, Príncipe JC, Ohnishi N - PLoS ONE (2011)

Comparing cardiac response (black line) with filter response (magenta line).The responses are shown in units representing the standard deviation. (A) The sympathetic system response to a stimulus intensity chosen from a continuous signal that was drawn randomly from a Gaussian distribution in contrast to a decoding filter response (SNR = 14.05 dB). (B) The corresponding vagal nerve (PNS) response and its estimated response using a decoding filter (SNR = 12.62 dB). Besides the fast oscillations, the decoding filters yielded a response the tracks fairly well the observed (physiological) cardiac response.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3111410&req=5

pone-0020227-g009: Comparing cardiac response (black line) with filter response (magenta line).The responses are shown in units representing the standard deviation. (A) The sympathetic system response to a stimulus intensity chosen from a continuous signal that was drawn randomly from a Gaussian distribution in contrast to a decoding filter response (SNR = 14.05 dB). (B) The corresponding vagal nerve (PNS) response and its estimated response using a decoding filter (SNR = 12.62 dB). Besides the fast oscillations, the decoding filters yielded a response the tracks fairly well the observed (physiological) cardiac response.
Mentions: where represents the estimated filter response and a scaling factor (see methods). We have to include the search for the best scaling factor because the ICA decomposition is blind to scaling. Using a dataset derived from rabbits in a time window (see methods), the decoding population overall response is smoother then the observed cardiac output. The estimated responses, for both sympathetic (Fig. 9A) and vagal (Fig. 9B) stimulation follow the expected heart response, but lack the fast oscillations. We also quantify the reliability of the decoding response by measuring the relationship between the cardiac response and the estimated noise in (3) using the signal-to-noise ratio, SNR(dB) , by sliding a time-window length of 270 seconds over 24 heart rate intervals. The SNR varies from to dB ( Fig. 10), which correspond to approximately and percent of accuracy in psychophysics [48] (see methods).

Bottom Line: Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm.We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation.Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

View Article: PubMed Central - PubMed

Affiliation: Biological Information Engineering Laboratory, Nagoya University, Nagoya, Aichi, Japan. lucena@ohnishi.nagoya-u.ac.jp

ABSTRACT
The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

Show MeSH