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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

What we are missing in our clinical assessments and basic research (data obtained from publicly available records registered to build the WHO charts). Stochastic, non-linear, dynamic processes clearly underlie the existing data that is at present enforced to be deterministic, linear, and static. (A) Progression of the change in weight day by day in male and female newborn babies according to the median weight summary drawn from 26,985 babies/summary point (13,623 girls, 13,362 boys). Babies were longitudinally tracked for 24 months upon which cross-sectional data were used to build the charts up to 5 years of age (28, 30). Inset highlights the initial drop in weight. Several inflection points in this curve have the potential to reveal additional information, particularly the first one that separates males from females in early stages of neurodevelopment. (B) Inflection points in the curve tracking the generalized coefficient of variation from the weight data. Female babies reach the significant minimum at 224 days, almost a month earlier than male babies at 252 days. Left-top inset zooms in the data for the first month, showing that the two groups separate in the first week after birth. Right-bottom inset shows the non-linear nature of the rate of change in median weight (zooming into the first month as well). (C) The skewed nature of the probability distributions underlying the physical growth parameters can be captured by tracking the L parameter (the Box–Cox transformation power value to enforce symmetry in skewed probability distributions (31), see also Appendix quoting the Methods paper (30) “The assumption is that, after the appropriate power transformation, the data are closely approximated by a normal distribution”). Notice that as in all other parameters the required transformation power L is different for male and female babies, denoting different families of probability distributions underlying their physical growth (in this case specifically the weight). Points mark the days when the generalized coefficient of variation reached inflection points, thus marking critical significant departures in variability in males vs. females. Inset zooms into the 1-month period to highlight the first of such inflection points as early as the first week of life. (D) Tracking the median weight over the first 5 years of life. Points mark the change in the underlying variability according to the inflection point in the generalized coefficient of variation. Inset zooms into the first month of life to also highlight the days when the inflection points in the underlying variability were attained in the first week of life.
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FA1: What we are missing in our clinical assessments and basic research (data obtained from publicly available records registered to build the WHO charts). Stochastic, non-linear, dynamic processes clearly underlie the existing data that is at present enforced to be deterministic, linear, and static. (A) Progression of the change in weight day by day in male and female newborn babies according to the median weight summary drawn from 26,985 babies/summary point (13,623 girls, 13,362 boys). Babies were longitudinally tracked for 24 months upon which cross-sectional data were used to build the charts up to 5 years of age (28, 30). Inset highlights the initial drop in weight. Several inflection points in this curve have the potential to reveal additional information, particularly the first one that separates males from females in early stages of neurodevelopment. (B) Inflection points in the curve tracking the generalized coefficient of variation from the weight data. Female babies reach the significant minimum at 224 days, almost a month earlier than male babies at 252 days. Left-top inset zooms in the data for the first month, showing that the two groups separate in the first week after birth. Right-bottom inset shows the non-linear nature of the rate of change in median weight (zooming into the first month as well). (C) The skewed nature of the probability distributions underlying the physical growth parameters can be captured by tracking the L parameter (the Box–Cox transformation power value to enforce symmetry in skewed probability distributions (31), see also Appendix quoting the Methods paper (30) “The assumption is that, after the appropriate power transformation, the data are closely approximated by a normal distribution”). Notice that as in all other parameters the required transformation power L is different for male and female babies, denoting different families of probability distributions underlying their physical growth (in this case specifically the weight). Points mark the days when the generalized coefficient of variation reached inflection points, thus marking critical significant departures in variability in males vs. females. Inset zooms into the 1-month period to highlight the first of such inflection points as early as the first week of life. (D) Tracking the median weight over the first 5 years of life. Points mark the change in the underlying variability according to the inflection point in the generalized coefficient of variation. Inset zooms into the first month of life to also highlight the days when the inflection points in the underlying variability were attained in the first week of life.

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
What we are missing in our clinical assessments and basic research (data obtained from publicly available records registered to build the WHO charts). Stochastic, non-linear, dynamic processes clearly underlie the existing data that is at present enforced to be deterministic, linear, and static. (A) Progression of the change in weight day by day in male and female newborn babies according to the median weight summary drawn from 26,985 babies/summary point (13,623 girls, 13,362 boys). Babies were longitudinally tracked for 24 months upon which cross-sectional data were used to build the charts up to 5 years of age (28, 30). Inset highlights the initial drop in weight. Several inflection points in this curve have the potential to reveal additional information, particularly the first one that separates males from females in early stages of neurodevelopment. (B) Inflection points in the curve tracking the generalized coefficient of variation from the weight data. Female babies reach the significant minimum at 224 days, almost a month earlier than male babies at 252 days. Left-top inset zooms in the data for the first month, showing that the two groups separate in the first week after birth. Right-bottom inset shows the non-linear nature of the rate of change in median weight (zooming into the first month as well). (C) The skewed nature of the probability distributions underlying the physical growth parameters can be captured by tracking the L parameter (the Box–Cox transformation power value to enforce symmetry in skewed probability distributions (31), see also Appendix quoting the Methods paper (30) “The assumption is that, after the appropriate power transformation, the data are closely approximated by a normal distribution”). Notice that as in all other parameters the required transformation power L is different for male and female babies, denoting different families of probability distributions underlying their physical growth (in this case specifically the weight). Points mark the days when the generalized coefficient of variation reached inflection points, thus marking critical significant departures in variability in males vs. females. Inset zooms into the 1-month period to highlight the first of such inflection points as early as the first week of life. (D) Tracking the median weight over the first 5 years of life. Points mark the change in the underlying variability according to the inflection point in the generalized coefficient of variation. Inset zooms into the first month of life to also highlight the days when the inflection points in the underlying variability were attained in the first week of life.
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FA1: What we are missing in our clinical assessments and basic research (data obtained from publicly available records registered to build the WHO charts). Stochastic, non-linear, dynamic processes clearly underlie the existing data that is at present enforced to be deterministic, linear, and static. (A) Progression of the change in weight day by day in male and female newborn babies according to the median weight summary drawn from 26,985 babies/summary point (13,623 girls, 13,362 boys). Babies were longitudinally tracked for 24 months upon which cross-sectional data were used to build the charts up to 5 years of age (28, 30). Inset highlights the initial drop in weight. Several inflection points in this curve have the potential to reveal additional information, particularly the first one that separates males from females in early stages of neurodevelopment. (B) Inflection points in the curve tracking the generalized coefficient of variation from the weight data. Female babies reach the significant minimum at 224 days, almost a month earlier than male babies at 252 days. Left-top inset zooms in the data for the first month, showing that the two groups separate in the first week after birth. Right-bottom inset shows the non-linear nature of the rate of change in median weight (zooming into the first month as well). (C) The skewed nature of the probability distributions underlying the physical growth parameters can be captured by tracking the L parameter (the Box–Cox transformation power value to enforce symmetry in skewed probability distributions (31), see also Appendix quoting the Methods paper (30) “The assumption is that, after the appropriate power transformation, the data are closely approximated by a normal distribution”). Notice that as in all other parameters the required transformation power L is different for male and female babies, denoting different families of probability distributions underlying their physical growth (in this case specifically the weight). Points mark the days when the generalized coefficient of variation reached inflection points, thus marking critical significant departures in variability in males vs. females. Inset zooms into the 1-month period to highlight the first of such inflection points as early as the first week of life. (D) Tracking the median weight over the first 5 years of life. Points mark the change in the underlying variability according to the inflection point in the generalized coefficient of variation. Inset zooms into the first month of life to also highlight the days when the inflection points in the underlying variability were attained in the first week of life.
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