Limits...
Neonatal Diagnostics: Toward Dynamic Growth Charts of Neuromotor Control

View Article: PubMed Central - PubMed

ABSTRACT

The current rise of neurodevelopmental disorders poses a critical need to detect risk early in order to rapidly intervene. One of the tools pediatricians use to track development is the standard growth chart. The growth charts are somewhat limited in predicting possible neurodevelopmental issues. They rely on linear models and assumptions of normality for physical growth data – obscuring key statistical information about possible neurodevelopmental risk in growth data that actually has accelerated, non-linear rates-of-change and variability encompassing skewed distributions. Here, we use new analytics to profile growth data from 36 newborn babies that were tracked longitudinally for 5 months. By switching to incremental (velocity-based) growth charts and combining these dynamic changes with underlying fluctuations in motor performance – as the transition from spontaneous random noise to a systematic signal – we demonstrate a method to detect very early stunting in the development of voluntary neuromotor control and to flag risk of neurodevelopmental derail.

No MeSH data available.


Related in: MedlinePlus

Statistical platform for the personalized analyses of natural behaviors: (A) the prevalent “one-size-fits-all” model currently in use to analyze kinematics data. The example shows epochs of temporal speed profiles aligned and averaged under the assumption of normality. The assumed (theoretical mean) and the assumed variance are then used to characterize the motor behavior, thus leaving out important fluctuations in motor performance (considered as noise or a nuisance). (B) Our proposed platform extracts waveforms of variations in motor performance and estimates the underlying family of probability distributions. This method characterizes the individual and the rate of change in PDFs as the noise (dispersion) decreases, and the signal becomes well-structured and systematic (predictable).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5120129&req=5

Figure 1: Statistical platform for the personalized analyses of natural behaviors: (A) the prevalent “one-size-fits-all” model currently in use to analyze kinematics data. The example shows epochs of temporal speed profiles aligned and averaged under the assumption of normality. The assumed (theoretical mean) and the assumed variance are then used to characterize the motor behavior, thus leaving out important fluctuations in motor performance (considered as noise or a nuisance). (B) Our proposed platform extracts waveforms of variations in motor performance and estimates the underlying family of probability distributions. This method characterizes the individual and the rate of change in PDFs as the noise (dispersion) decreases, and the signal becomes well-structured and systematic (predictable).

Mentions: Neurodevelopment follows an extremely dynamic trajectory (1–4), with each infant experiencing a range of unique changes, driven by both the infant and their own environment. During the early stages of neurodevelopment, the infant’s body and head grow at an accelerated rate (e.g., see Figure A1), and the nervous systems of the infant must rapidly develop in tandem to adapt to, and to compensate for, these changes. Due to the variable nature of biological systems, these day-to-day fluctuations in physical growth follow a non-uniform, non-linear process, with some babies changing at slower rate than others at certain times. Likewise, the fast-changing nervous systems underlying the fast-growing physical body must develop rapidly to create the foundation for purposeful controlled actions. In the face of such highly variable neurodevelopmental processes, it may be important to switch from the “one-size-fits-all” model currently in use (Figure 1A) to a personalized statistical approach (Figure 1B). In particular, the use of a personalized approach is more adequate to individually fit, and thus “capture,” the true nature of the adaptive processes of the early stages of a newborn’s life.


Neonatal Diagnostics: Toward Dynamic Growth Charts of Neuromotor Control
Statistical platform for the personalized analyses of natural behaviors: (A) the prevalent “one-size-fits-all” model currently in use to analyze kinematics data. The example shows epochs of temporal speed profiles aligned and averaged under the assumption of normality. The assumed (theoretical mean) and the assumed variance are then used to characterize the motor behavior, thus leaving out important fluctuations in motor performance (considered as noise or a nuisance). (B) Our proposed platform extracts waveforms of variations in motor performance and estimates the underlying family of probability distributions. This method characterizes the individual and the rate of change in PDFs as the noise (dispersion) decreases, and the signal becomes well-structured and systematic (predictable).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120129&req=5

Figure 1: Statistical platform for the personalized analyses of natural behaviors: (A) the prevalent “one-size-fits-all” model currently in use to analyze kinematics data. The example shows epochs of temporal speed profiles aligned and averaged under the assumption of normality. The assumed (theoretical mean) and the assumed variance are then used to characterize the motor behavior, thus leaving out important fluctuations in motor performance (considered as noise or a nuisance). (B) Our proposed platform extracts waveforms of variations in motor performance and estimates the underlying family of probability distributions. This method characterizes the individual and the rate of change in PDFs as the noise (dispersion) decreases, and the signal becomes well-structured and systematic (predictable).
Mentions: Neurodevelopment follows an extremely dynamic trajectory (1–4), with each infant experiencing a range of unique changes, driven by both the infant and their own environment. During the early stages of neurodevelopment, the infant’s body and head grow at an accelerated rate (e.g., see Figure A1), and the nervous systems of the infant must rapidly develop in tandem to adapt to, and to compensate for, these changes. Due to the variable nature of biological systems, these day-to-day fluctuations in physical growth follow a non-uniform, non-linear process, with some babies changing at slower rate than others at certain times. Likewise, the fast-changing nervous systems underlying the fast-growing physical body must develop rapidly to create the foundation for purposeful controlled actions. In the face of such highly variable neurodevelopmental processes, it may be important to switch from the “one-size-fits-all” model currently in use (Figure 1A) to a personalized statistical approach (Figure 1B). In particular, the use of a personalized approach is more adequate to individually fit, and thus “capture,” the true nature of the adaptive processes of the early stages of a newborn’s life.

View Article: PubMed Central - PubMed

ABSTRACT

The current rise of neurodevelopmental disorders poses a critical need to detect risk early in order to rapidly intervene. One of the tools pediatricians use to track development is the standard growth chart. The growth charts are somewhat limited in predicting possible neurodevelopmental issues. They rely on linear models and assumptions of normality for physical growth data – obscuring key statistical information about possible neurodevelopmental risk in growth data that actually has accelerated, non-linear rates-of-change and variability encompassing skewed distributions. Here, we use new analytics to profile growth data from 36 newborn babies that were tracked longitudinally for 5 months. By switching to incremental (velocity-based) growth charts and combining these dynamic changes with underlying fluctuations in motor performance – as the transition from spontaneous random noise to a systematic signal – we demonstrate a method to detect very early stunting in the development of voluntary neuromotor control and to flag risk of neurodevelopmental derail.

No MeSH data available.


Related in: MedlinePlus