title: Biocomplexity of respiratory neural network during early maturation.
abstract: Previous studies in various animal models have shown that respiratory premotor and motor neurons undergo rapid changes in biochemical and bioelectric properties during the first month of postnatal life. Early in postnatal life, there is an increase in the complexity of the morphology of the dendritic tree of respiratory neurons as it changes from a bipolar to a multipolar morphology. During normal breathing (eupnea), the phrenic nerve has a slow, ramping output which reflects the orderly recruitment of phrenic motoneurons throughout inspiration when viewed in the time domain. Hypercapnia stimulates the respiratory system increasing both the respiratory frequency and amplitude. During severe hypoxia, the output of the phrenic nerve initially falls to zero and then returns with a completely different firing pattern, gasping. During gasping the phrenic nerve fires with an abrupt onset and rises rapidly to maximal neural activity with a decrementing decline. During recovery from hypoxia a variety of respiratory patterns between eupnea and gasping are seen in the time domain. Both the time and frequency analysis methods, however, give little information about the system generating the output. In contrast, the nonlinear dynamic neural analysis method we propose has been found to be a useful method for quantifying the complexity (irregularity) of both physical systems and physiological signals. The respiratory motor output depends on the integrated properties of the neural network.
authors.affiliation: Thayer School of Engineering, Dartmouth College, 8000 Cummings Hall. Hanover, New Hampshire 03755 USA. Metin.Akay@Dartmouth.Edu.
authors.forename: Metin
authors.initials: M
authors.lastname: Akay