Origins and early development of human body knowledge

As a knowable object, the human body is highly complex. Evidence from several converging lines of research, including psychological studies, neuroimaging and clinical neuropsychology, indicates that human body knowledge is widely distributed in the adult brain, and is instantiated in at least three partially independent levels of representation. Sensori-motor body knowledge is responsible for on-line control and movement of one's own body and may also contribute to the perception of others' moving bodies; visuo-spatial body knowledge specifies detailed structural descriptions of the spatial attributes of the human body; and lexical-semantic body knowledge contains language-based knowledge about the human body. In the first chapter of this Monograph, we outline the evidence for these three hypothesized levels of human body knowledge, then review relevant literature on infants' and young children's human body knowledge in terms of the three-level framework. In Chapters II and III, we report two complimentary series of studies that specifically investigate the emergence of visuospatial body knowledge in infancy. Our technique is to compare infants' responses to typical and scrambled human bodies, in order to evaluate when and how infants acquire knowledge about the canonical spatial layout of the human body. Data from a series of visual habituation studies indicate that infants first discriminate scrambled from typical human body pictures at 15 to 18 months of age. Data from object examination studies similarly indicate that infants are sensitive to violations of three-dimensional human body stimuli starting at 15-18 months of age. The overall pattern of data supports several conclusions about the early development of human body knowledge: (a) detailed visuo-spatial knowledge about the human body is first evident in the second year of life, (b) visuo-spatial knowledge of human faces and human bodies are at least partially independent in infancy and (c) infants' initial visuo-spatial human body representations appear to be highly schematic, becoming more detailed and specific with development. In the final chapter, we explore these conclusions and discuss how levels of body knowledge may interact in early development.

Transmission ratio distortion in the human body louse, Pediculus humanus (Insecta : Phthiraptera)

We studied inheritance at three microsatellite loci in eight F-1 and two F-2 families of the body (clothes) louse of humans, Pediculus humanus. The alleles of heterozygous female-parents were always inherited in a Mendelian fashion in these families. Alleles from heterozygous male-parents, however, were inherited in two different ways: (i) in a Mendelian fashion and (ii) in a non-Mendelian fashion, where males passed to their offspring only one of their two alleles, that is, 100% nonrandom transmission. In male body lice, where there was non-Mendelian inheritance, the paternally inherited set of alleles was eliminated. We interpret this pattern of inheritance as evidence for extreme transmission ratio distortion of paternal alleles in this species.

Muscle fiber and motor unit behavior in the longest human skeletal muscle

The sartorius muscle is the longest muscle in the human body. It is strap-like, up to 600 mm in length, and contains five to seven neurovascular compartments, each with a neuromuscular endplate zone. Some of its fibers terminate intrafascicularly, whereas others may run the full length of the muscle. To assess the location and timing of activation within motor units of this long muscle, we recorded electromyographic potentials from multiple intramuscular electrodes along sartorius muscle during steady voluntary contraction and analyzed their activity with spike-triggered averaging from a needle electrode inserted near the proximal end of the muscle. Approximately 30% of sartorius motor units included muscle fibers that ran the full length of the muscle, conducting action potentials at 3.9 +/- 0.1 m/s. Most motor units were innervated within a single muscle endplate zone that was not necessarily near the midpoint of the fiber. As a consequence, action potentials reached the distal end of a unit as late as 100 ms after initiation at an endplate zone. Thus, contractile activity is not synchronized along the length of single sartorius fibers. We postulate that lateral transmission of force from fiber to endomysium and a wide distribution of motor unit endplates along the muscle are critical for the efficient transmission of force from sarcomere to tendon and for the prevention of muscle injury caused by overextension of inactive regions of muscle fibers.

Numerical modelling of thermal effects in rats due to high-field magnetic resonance imaging (0.5-1 GHz)

A finite-difference time-domain (FDTD) thermal model has been developed to compute the temperature elevation in the Sprague Dawley rat due to electromagnetic energy deposition in high-field magnetic resonance imaging (MRI). The field strengths examined ranged from 11.75-23.5 T (corresponding to H-1 resonances of 0.5-1 GHz) and an N-stub birdcage resonator was used to both transmit radio-frequency energy and receive the MRI signals. With an in-plane resolution of 1.95 mm, the inhomogeneous rat phantom forms a segmented model of 12 different tissue types, each having its electrical and thermal parameters assigned. The steady-state temperature distribution was calculated using a Pennes 'bioheat' approach. The numerical algorithm used to calculate the induced temperature distribution has been successfully validated against analytical solutions in the form of simplified spherical models with electrical and thermal properties of rat muscle. As well as assisting with the design of MRI experiments and apparatus, the numerical procedures developed in this study could help in future research and design of tumour-treating hyperthermia applicators to be used on rats in vivo.

A distributed equivalent magnetic current based FDTD method for the calculation of E-fields induced by gradient coils

This paper evaluates a new, low-frequency finite-difference time-domain method applied to the problem of induced E-fields/eddy currents in the human body resulting from the pulsed magnetic field gradients in MRI. In this algorithm, a distributed equivalent magnetic current is proposed as the electromagnetic source and is obtained by quasistatic calculation of the empty coil's vector potential or measurements therein. This technique circumvents the discretization of complicated gradient coil geometries into a mesh of Yee cells, and thereby enables any type of gradient coil modelling or other complex low frequency sources. The proposed method has been verified against an example with an analytical solution. Results are presented showing the spatial distribution of gradient-induced electric fields in a multi-layered spherical phantom model and a complete body model. (C) 2004 Elsevier Inc. All rights reserved.

Electromagnetic fields inside a lossy, multilayered spherical head phantom excited by MRI coils: models and methods

The precise evaluation of electromagnetic field (EMF) distributions inside biological samples is becoming an increasingly important design requirement for high field MRI systems. In evaluating the induced fields caused by magnetic field gradients and RF transmitter coils, a multilayered dielectric spherical head model is proposed to provide a better understanding of electromagnetic interactions when compared to a traditional homogeneous head phantom. This paper presents Debye potential (DP) and Dyadic Green's function (DGF)-based solutions of the EMFs inside a head-sized, stratified sphere with similar radial conductivity and permittivity profiles as a human head. The DP approach is formulated for the symmetric case in which the source is a circular loop carrying a harmonic-formed current over a wide frequency range. The DGF method is developed for generic cases in which the source may be any kind of RF coil whose current distribution can be evaluated using the method of moments. The calculated EMFs can then be used to deduce MRI imaging parameters. The proposed methods, while not representing the full complexity of a head model, offer advantages in rapid prototyping as the computation times are much lower than a full finite difference time domain calculation using a complex head model. Test examples demonstrate the capability of the proposed models/methods. It is anticipated that this model will be of particular value for high field MRI applications, especially the rapid evaluation of RF resonator (surface and volume coils) and high performance gradient set designs.

An FDTD model for calculation of gradient-induced eddy currents in MRI system

In most magnetic resonance imaging (MRI) systems, pulsed magnetic gradient fields induce eddy currents in the conducting structures of the superconducting magnet. The eddy currents induced in structures within the cryostat are particularly problematic as they are characterized by long time constants by virtue of the low resistivity of the conductors. This paper presents a three-dimensional (3-D) finite-difference time-domain (FDTD) scheme in cylindrical coordinates for eddy-current calculation in conductors. This model is intended to be part of a complete FDTD model of an MRI system including all RF and low-frequency field generating units and electrical models of the patient. The singularity apparent in the governing equations is removed by using a series expansion method and the conductor-air boundary condition is handled using a variant of the surface impedance concept. The numerical difficulty due to the asymmetry of Maxwell equations for low-frequency eddy-current problems is circumvented by taking advantage of the known penetration behavior of the eddy-current fields. A perfectly matched layer absorbing boundary condition in 3-D cylindrical coordinates is also incorporated. The numerical method has been verified against analytical solutions for simple cases. Finally, the algorithm is illustrated by modeling a pulsed field gradient coil system within an MRI magnet system. The results demonstrate that the proposed FDTD scheme can be used to calculate large-scale eddy-current problems in materials with high conductivity at low frequencies.

Perception of faces and bodies: Similar or different?

Human faces and bodies are both complex and interesting perceptual objects, and both convey important social information. Given these similarities between faces and bodies, we can ask how similar are the visual processing mechanisms used to recognize them. It has long been argued that faces are subject to dedicated and unique perceptual processes, but until recently, relatively little research has focused on how we perceive the human. body. Some recent paradigms indicate that faces and bodies are processed differently; others show similarities in face and body perception. These similarities and differences depend on the type of perceptual task and the level of processing involved. Future research should take these issues into account.

How primatology can inform us about the evolution of the humand mind

Humans are primates. We have evolved from common ancestors and the evolution of the human body is becoming increasingly clear as the archeological record expands. But for most people the gap between humans and animals lies in the mind, not in the body. And minds do not fossilise. To reconstruct the evolution of mind, scholars have thus increasingly looked to our closest relatives for clues. Here I discuss four ways in which the study of primates may inform such reconstruction: fact-finding, phylogenetic reconstruction, analogy, and regression models. Knowledge about primates can help us bridge the gap. Extinction of our closest relatives, on the other hand, would not only deplete that source of information but also increase the apparent differences between animal and human minds. It is likely that we have a long history of displacing closely related species, including the other hominids, leading us to appear ever more unique.

Quantification of lean bodyweight

Background: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens. Objective: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW. Patients and methods: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA - a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18-82 years), bodyweight (40.7-216.5kg) and BMI values (17.1-69.9 kg/m(2)). Patients in population B had BMI values of 18.7-38.4 kg/m(2). A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B. Results: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r(2) = 0.78, mean error [ME] = 2.30 x 10(-3), root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r(2) = 0.93, ME = -0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r(2) = 0.85, ME -0.04, RMSE = 4.39 [approximately 7% of mean]). Conclusions: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.