All-atom molecular dynamics simulations using orientational constraints from anisotropic NMR samples

Orientational constraints obtained from solid state NMR experiments on anisotropic samples are used here in molecular dynamics (MD) simulations for determining the structure and dynamics of several different membrane-bound molecules. The new MD technique is based on the inclusion of orientation dependent pseudo-forces in the COSMOS-NMR force field. These forces drive molecular rotations and re-orientations in the simulation, such that the motional time-averages of the tensorial NMR properties approach the experimentally measured parameters. The orientational-constraint-driven MD simulations are universally applicable to all NMR interaction tensors, such as chemical shifts, dipolar couplings and quadrupolar interactions. The strategy does not depend on the initial choice of coordinates, and is in principle suitable for any flexible molecule. To test the method on three systems of increasing complexity, we used as constraints some deuterium quadrupolar couplings from the literature on pyrene, cholesterol and an antimicrobial peptide embedded in oriented lipid bilayers. The MD simulations were able to reproduce the NMR parameters within experimental error. The alignment of the three membrane-bound molecules and some aspects of their conformation were thus derived from the NMR data, in good agreement with previous analyses. Furthermore, the new approach yielded for the first time the distribution of segmental orientations with respect to the membrane and the order parameter tensors of all three systems.
Introduction
Using anisotropic media, such as partially oriented bicelles or macroscopically oriented membranes, NMR investigations can reveal a wealth of information about molecular properties, namely conformation, orientation and dynamics. In many solid state NMR studies of membrane-active peptides and transmembrane proteins, the samples are conveniently prepared with macroscopically oriented bilayers to obtain structural information. The NMR data analysis relies on a uniform alignment of all molecules with respect to the static magnetic field, as it makes use of the orientation dependence of the chemical shift, quadrupolar coupling or dipolar coupling interactions. In contrast to single crystal studies, where the molecules are immobilized in a unique conformation, in the case of lipid membranes and liquid crystalline systems one has to consider a wide distribution of molecular orientations and anisotropic motions. Here, we apply for the first time a new MD strategy to deduce such structural and dynamics information on three representative compounds in biomembranes with increasing complexity: (i) pyrene, (ii) cholesterol, and (iii) the antimicrobial peptide PGLa.
A convenient nucleus for solid state NMR investigations of oriented membranes is deuterium for several reasons (for reviews see e.g., Vold 1994; Davis 1983; Ulrich and Grage 1998). First, the influence of 2H substitutions on the molecular structure and dynamics is negligible. More importantly, in most biologically relevant cases the quadrupolar interaction tensor of a carbon-bound deuterium is to a good approximation aligned parallel to the 2H–C bond and directly reflects the local molecular orientation and dynamics of the labelled segment. Using 2H-NMR, the alignment and dynamic behaviour of lipids, small organic guest molecules and membrane-active peptides has been studied in numerous examples. Most previous strategies to extract the molecular orientation from NMR data, however, have been restricted to molecules with a rigid conformation, e.g. peptides assuming an ideal α-helix with fixed backbone torsion angles. In these studies, several individual labels are usually placed into strategic positions on the rigid molecular part. The measured quadrupolar splittings are then compared in a least squares RMSD analysis with the predicted values upon systematically varying the molecular alignment. The best-fit molecular orientation is determined from a 3D error plot as the one yielding minimal differences between the observed and calculated NMR parameters. However, this RMSD (root mean square deviation) analysis can only give preliminary results for flexible molecules such as peptides or proteins with a high degree of internal mobility.
A proper way to account for motional averaging of the NMR parameters caused by molecular mobility is to run all-atom MD simulations. When applied to biomembranes, however, such simulations pose two closely connected problems: (i) large size of the system and (ii) long simulation times. In particular, water surrounding the lipid bilayer has to be included into the MD simulations, and time-spans have to be covered approaching the NMR time scale. Moreover, the volume of interest has to be surrounded by multiple copies of the central cell to avoid boundary effects. Nevertheless, such simulations have been demonstrated to be feasible for moderately sized membrane segments hosting medium size molecules (see e.g. Tieleman et al. 2001). In the present contribution, we propose an alternative strategy, in which the oriented medium is not explicitly considered, but instead the ordering membrane environment is replaced in the MD simulations by pseudo-forces derived from the measured NMR parameters. This way sufficiently long MD runs become possible and allow to calculate the motionally averaged parameters observed in the NMR experiments. As a result, the full information about molecular orientation, order, segmental motions and even aspects of the molecular conformation could be revealed.
There is one remarkable advantage in the calculations involving orientational constraints. Since an experimentally observed NMR value usually represents a time average over the motions of the molecules, knowledge of the geometry and strength of these molecular motions is a prerequisite for properly interpreting the NMR data in terms of structural parameters. The order parameter, which is typically used to describe the effect of molecular motion on the NMR spectra, however, is often not known a priori. In the present approach, the molecular motions are intrinsically included and the order parameters are obtained as part of the simulation result.
Theory
NMR interaction tensors and coordinate transformations
The orientation dependence of the observed frequencies or splittings of an NMR property can be described by a 2nd rank tensor P, whose components are specified in the context of a coordinate system. On the one hand, a description in a molecule-fixed frame of reference is required to follow changes in orientation, but on the other hand the NMR observables are extracted from the interaction tensor in the laboratory frame related to the static magnetic field. We will define the molecule-fixed frames in the following way from the coordinates of the three bonded atoms A–B–C (see Fig. 1):
Fig. 1Local coordinate system used to define a tensorial NMR property on atom A. The bond from A to atom B is taken as the local z-direction, the plane spanned by A, B and C defines the x-direction in-plane, and the y-direction is perpendicular to that plane
Atom A is selected as the position of the nucleus of interest (for instance 2H), whose property PA is to be calculated. In case there are several possible ways of selecting atoms B and C, they will be defined such that the valence of bond B–C is a maximum (the valence can be estimated from the bond length, see O’Keefe and Brese 1991). This selection ensures that the y-axis will be perpendicular to a π-system if it exists, and the x-axis will be positioned within the π-plane.
Once we have selected a coordinate system that is fixed to the molecule, we can calculate the columns of the transformation matrix D from the unit vectors along  of this frame, given in coordinates of the laboratory frame.
Matrix D can be used to transform the tensorial properties from the molecular to the laboratory frame. The tensor components of P in the molecular frame are thus transformed to yield P′ in the laboratory frame according to
where D is the transformation matrix as given in Eq. 2 (the Einstein sum convention is used for Greek subscripts and α, β = {x, y, z}, the second index indicating the unit vector).
For most deuterium quadrupolar couplings, as well as vicinal dipolar couplings like 15N–1H and 13C–1H the tensor PA will be to a good approximation diagonal in this coordinate system, hence we can easily assign the principal values of PA to the three axes. In cases where the chemical shift tensor is to be analysed, the same molecular coordinate system can be used as in those calculations based on the bond polarization theory (BPT, see Sternberg 1988). Therefore, all these types of NMR interactions can be treated within the same formalism.
In static NMR experiments the tensor components are observed in a coordinate system of reference whose z-axis is aligned with B0. In this frame the measured frequency or splitting is given by the zz-component of the tensor P. However, in the approach presented here we can incorporate further tensor components in the MD simulations, which are accessible for example by measuring the sample at further tilt angles.
Constraints and calculation of molecular properties
To include experimental constraints in the MD simulations, pseudo-energy terms are added to the molecular energy provided by the force field. These pseudo-energies are defined as functions of the difference between the experimental and a calculated tensor property, Pi:
where k is a force constant which is chosen to adjust the size and unit of the energy. The first sum runs over all observed tensor components and does not need to include all possible elements. In a 1D NMR measurement of a single label in an oriented or monocrystalline sample, only the zz-value of the tensor of this site can be experimentally determined. However, any additional tensor component introduced into the calculations will improve the characterization of the orientation of the system. Because of the nature of the tensor transform (Eq. 3) each principal axis of the tensor can be inverted without changing the observed NMR frequency. Therefore, eight discrete tensor orientations are equivalent, even if all elements of the observed tensor are used as constraints. If, however, only the three principal values of the tensor are known, then in addition to this eightfold degeneracy a continuous set of orientations (instead of discrete possibilities) will be compatible with the experimental data from a single label. Therefore, a larger number of orientational constraints from several labels are necessary to determine the orientation of a rigid molecular segment in an unambiguous way. Obviously, if a molecule is intrinsically flexible, an even higher number of constraints will be required.
The experimental NMR values represent an ensemble average over a large number of molecules as well as a time average. The relevant time scale for averaging is given by the inverse of the frequency range that results from the anisotropy of the relevant interaction. When motions are faster than this time scale, a time average of the corresponding NMR parameters is observed. Motions slower than this time scale will result in a distribution of the respective NMR property. In all-atom MD simulations of larger molecules we can only perform calculations for a single molecular system and for time spans on the order of a Larmor period. Assuming that the NMR interactions are already completely averaged within this time, averaged properties can be simulated. This is likely to be the case for motions within the molecule and for the rotational diffusion of small molecules in oriented media such as the lipid bilayer considered here, which are on the time scale of 30 ps to 100 ns (see Brüschweiler 1994). We thus can assume that the time averages over the shorter period accessible for MD simulations do reflect the longer time averages observed by the NMR measurement. For the average of Ptheo we obtain using Eq. 3:
If the NMR property does not change with time, we have to perform the time averaging only over the products of the transformation matrix elements . For most deuterium quadrupolar couplings and vicinal dipolar couplings, any conformational influence on the NMR interaction can be neglected. In the case of chemical shift tensors, however, the change of the tensors in the course of the MD simulation has to be considered.
Since MD simulations are started with random velocities, we have to run many time steps before a thermal equilibrium is reached, and before the kinetic energy is redistributed properly between the different degrees of freedom of our system. During this equilibration phase of the MD the pseudo-energies will be large and unrealistic, since the proper mean values for the interaction tensors are not yet reached. Therefore the time averaging is performed here using an exponential memory function (Torda and van Gunsteren 1991):
The memory time constant is denoted with τ, and N is the norm of the integral. This memory function serves to scale down the contributions from the beginning of the MD calculation exponentially. By proper selection of τ it can be ensured that we average mainly over the late, relevant time span of interest. Since in conventional MD simulations the equations of motion are integrated in finite time steps Δt, the integral in Eq. 6 is converted into a discrete sum S. During the MD simulation, the sum Sn+1 of the time step n + 1 is calculated from the sum of the previous time step Sn in a recursive manner:
In the course of the MD simulation, S will contain the exponentially weighted time average of the property P, and in the case of a tensor the time averaging will be performed with all of its components.
Calculation of pseudo-forces
To include the NMR constraints into the equations of motion, pseudo-forces  have to be calculated from the respective pseudo-energies (Eq. 4). They are obtained as the derivatives of the energies with respect to the coordinates of the atoms. In the case of orientational pseudo-forces, we have to derive the transformation matrices D with respect to the coordinates of the atoms that were used in their definition (see Eq. 1). The pseudo-forces in the Cartesian directions x, y and z (denoted with the Greek index γ) acting on the atoms A, B, and C (denoted with j, see Eq. 1) are then given by
The calculation of the orientational pseudo-forces is thus reduced to determining the derivatives of the elements of the transformation matrices D with respect to the Cartesian coordinates of atoms A, B, and C.
In the following expressions we will use a short notation for the derivatives, omitting all indices:
Using the definitions of Eqs. 1 and 9, these derivatives can be calculated in the following way:
The derivatives of the unit vectors  can be used to construct the derivatives of the elements of the transformation matrices .
Because of the time dependence of the transformation matrices, the derivatives were calculated continuously during the MD simulation at each time step. The time average is only calculated for the NMR property.
As evident from Eq. 8, the pseudo-forces depend linearly on the difference between theory and experiment. To avoid too high pseudo-forces in the beginning of the MD simulation, when the deviation of the simulated and experimental values is still large, the contributions of the pseudo-forces are scaled down. Another possibility to avoid too large pseudo-forces is achieved by multiplying the pseudo-forces by weighting factors , which lead to nearly constant forces if  exceeds a threshold 	ΔP (see Witter et al. 2001):
For  this factor behaves similar to the derivative of the original energy expression (4). It is possible to choose different force constants k (Eq. 8) and width parameters ΔP for each type of NMR property of interest.
Order parameter calculation
Besides determining the structure of a molecule, it is equally important to describe its dynamic behaviour in an oriented medium such as a lipid bilayer. MD simulations provide a unique opportunity to obtain information on the molecular dynamics, which is difficult to determine experimentally. The traditional way to account for motional averages in NMR investigation is to introduce order parameters. To this aim, the time average of the transformation matrices D (Eq. 5) can be used to characterize the influence of restricted molecular motions on the anisotropic properties of a molecule. In a similar way, Saupe (1964) defined the so called order tensor to describe the average orientation of a molecule:
where 〈 〉 denotes the mean value and cosΘi are the z components of the unit vectors of a molecule-fixed frame within the laboratory system of reference. To describe the dynamic properties of flexible molecules, however, the Saupe approach is not sufficient, since molecular groups like peptide side chains may exhibit a larger mobility than the rest of the molecule. For this purpose Limmer (1989) introduced a segmental or site-specific order tensor W, which is the tensorial product of the Saupe order tensor and a conformational order tensor.
By calculating the NMR properties in the course of the MD simulation it is possible to obtain the segmental order tensor WA of the vicinity of atom A. The principal axis system of the tensor property is used to represent the instantaneous segmental orientation, which is then related to the average tensor P determined by Eq. 5. The transformation matrices DPAS, which link the average P with the principal axis system of P at each time step of the simulation, can then be used to determine W (〈 〉t denotes the time average):
This average is calculated in every step of the MD simulation for all sites A for which the tensorial NMR properties are calculated. In general the complete order tensor has 81 elements, which reduce to nine components if we are interested only in the zz component of a property (Eq. 13). Since the order tensor W (and S as well) is symmetric and traceless, we have to consider only five independent elements. The transformation matrices DPAS in Eq. 13 can be calculated from the eigenvectors corresponding to the principal values of PA. After a full-length MD simulation, the order tensors WA will contain the information describing the time averaged orientation of all sites A.
To calculate the molecular Saupe order tensor, a coordinate system has to be fixed to the molecule. In a perfectly rigid molecule any set of three atoms could be selected at will. However, as most molecules are intrinsically flexible, it does not make sense to define the coordinate system that way, as the Saupe order tensor would then depend on this selection. We therefore decided to use the principal axis system of the tensor of inertia to represent the orientation of the entire molecule (see Burnell and De Lange 1980). This way we can calculate a molecular order tensor that is independent of any particular choice of coordinates:
The axes of inertia are denoted with a, b and c, corresponding to the moments of inertia in increasing order. For rod-like molecules the long axis will thus be the a-axis of least inertia.
In many cases it is convenient to diagonalize the order tensor to introduce a principal frame of order (Low 2002). Within this frame there are only two independent parameters: the order parameter S and a biaxality parameter ξ:
By ordering the principal components as , one gets . If the biaxiality ξ is zero, the orientational distribution of the molecule is defined by a single axis, which is called director. In the biaxial case with a non-zero ξ, there exists a second distinct direction orthogonal to the director, and the molecular motions are characterized by two axes. A perfectly axially symmetric molecule that is oriented parallel to the membrane normal will lead to the following order tensor:
When the molecular Saupe tensor is used to interpret the NMR observables, the conformational tensors CA have to be known as well. These tensors mediate between the orientation and order of a site A and the molecular coordinate system. If the transformation matrices leading from the molecular coordinate system to the individual tensor systems of the sites A are known, CA can be calculated the same way as the order tensor (see Limmer 1989). Only in the case of rigid molecules, simple expressions for CA can be derived from the molecular geometry. Since in our MD simulations the segmental order tensors WA and the molecular Saupe order tensor are calculated directly, the conformational order tensors become obsolete.
Molecular dynamics simulation
The integration of the equations of motions is based on Verlet’s algorithm (Verlet 1967), and time steps of 0.5 fs were employed to sample all high frequency hydrogen atom vibrations. In constrained MD simulations it is generally necessary to control the temperature during the simulation time. This is accomplished by coupling the molecular system to a heat bath which dissipates the heat generated by the pseudo-forces. All prevailing differences between the constraints and their calculated values are sources of heat. To obtain an NTV assemble (with conserved particle number N, temperature T, and volume V), we introduced a proper thermostating procedure (see Evans and Morriss 1990). The coupling to the thermostat is controlled by a coupling time constant η which should be much larger than the time step. This time span η allows an adjustment of the range of thermal fluctuations in the simulated molecular system.
To prevent too large pseudo-forces at the start of the MD simulation the pseudo-forces were gradually increased towards their final values during the MD simulation. To this aim we introduced time dependent scaling factors , which approach the value 1.0 in an exponential fashion. The time constant was set in most cases to 200 ps, leading to a relatively smooth course of the temperature.
When applying the NMR orientational constraints during an MD run, the resulting pseudo-forces will “heat up” the system and enhance its rotational degrees of freedoms. Because the averaging procedure depends on the molecular re-orientations caused by the NMR constraints, some net rotational motion will prevail up to the end of the simulation. In standard MD simulations any overall molecular rotations and translations are subtracted from the velocities, since these external degrees of freedom are not of interest. In the present orientationally constrained calculations, however, only the net translations of the systems are removed.
Parametrization
For NMR spectroscopic considerations it is convenient to introduce the quadrupolar coupling tensor CQ in frequency units:
where Q is the quadrupolar moment of the deuterium nucleus, and V the electric field gradient tensor at the nuclear position. The trace of CQ is zero because of , and in the case of carbon-bonded deuterons the quadrupolar coupling tensor can be considered to a good approximation as diagonal in the coordinate system aligned with the C–2H bond. If the electron distribution is axially symmetric there is only one linearly independent tensor component, otherwise there are two. In the general high temperature case we observe two quadrupolar resonances with frequencies symmetric to zero:
If the z-axis of the local electric field gradient points into the direction of the magnetic field, the corresponding quadrupolar splitting Δ ν is
In the case of a C–2H bond the deuterium quadrupolar coupling depends only weakly on the molecular surrounding, hence representative values from static solid state NMR measurements of characteristic substances can be used as quadrupolar constants (Table 1). In this selection we have to keep in mind that the quadrupolar coupling constants depend strongly on the hybridization of the carbon, and to some extent also on the polarization of the C–2H bonds by partial charges caused by electronegative groups.
The coordinate system for the local tensors is defined in Fig. 1. The zz-component is aligned with the C–2H bond direction, and the yy-component is perpendicular to any π system. For an aromatic C–2H (sp2) bond an asymmetry parameter of  with ) was assumed, and the tensor axes were assigned according to calculations of Bailey (1998). The value for the –CD3 group is obtained from the C(sp3)–2H value scaled by 1/3, assuming rapid rotation around the C–CD3 axis.
Program implementation
The routines to apply the proposed orientational constraints are included into the COSMOS-NMR force field, which has been used in a number of previous applications, but which had so far been restricted to distance and chemical shift constraints (see e.g. Sternberg et al. 2003; Witter et al. 2002), and for the force field (Möllhoff and Sternberg 2001; Sternberg et al. 2001). These two types of NMR constraints can be combined with the new orientational constraints as will be demonstrated in the example of PGLa below. The authors provide the backend version of the full COSMOS program, containing all computational procedures without the graphics and modelling interface (GUI) (see http://www.cosmos-software.de). The COSMOS-backend (C++) was compiled for several operation systems including Windows, Unix and Linux.
Applications
The use of NMR orientational constraints is particularly well suited to gain insight into the alignment and dynamics of molecules embedded in biomembranes. In the three examples presented here, we will first demonstrate our new MD approach on pyrene as a simple model compound dissolved in lipid bilayers, then we will apply it to cholesterol as an intrinsic membrane lipid, and finally to the antimicrobial peptide PGLa, which forms an amphiphilic α-helix in membranes.
Pyrene
Pyrene was used as a first example, not only because of its relatively simple and rigid structure, but also because a full set of data is available from a recent 2H-NMR analysis in oriented lipid bilayers (Hoff et al. 2005). The same publication included an all-atom MD simulation of four pyrene molecules in a POPC (1-palmitoyl-2-oleoyl-phosphatedylcholine) membrane. Those previous results will thus allow a direct comparison of our new strategy with established simulation techniques. The present orientational constraint-driven simulation utilizes the experimental 2H-NMR data of deuterium-labelled pyrene (see Fig. 2) embedded at 4 mol% in POPC bilayers. Spectra had been acquired at two discrete sample orientations, namely with the membrane normal aligned parallel and perpendicular to the B0 field. In both orientations two pairs of narrow lines were observed (corresponding to the sites 2H1 and 2H2 + 2H3, respectively), whose splittings changed by a factor of 1/2. This situation can only originate from axially symmetric averaged quadrupolar tensors that are aligned with the membrane normal. This means that the pyrene molecules reorient quickly (on the time scale of the NMR interaction) about the membrane normal. Since the principal axis system of each averaged quadrupolar deuterium coupling tensor coincides with the membrane-fixed coordinate system, the absolute values of all 6 or 5 linearly independent components of the averaged coupling tensor are known and can be included in the present MD simulation. In this case, we could even make use of the signs of the principal quadrupolar components, as they had been deduced from the previous MD simulations mentioned above (Hoff et al. 2005).Fig. 2Fully deuterated pyrene molecule with three kinds of labelled sites (2H1, 2H2 and 2H3), illustrating the principal axes of the local deuterium quadrupolar tensors and of the principal axes of inertia a, b and c
Using the 2H-NMR data from the literature, we performed two MD simulations on pyrene constrained by the complete tensors. In the first run, only the contributions to the pseudo-energy arising from the three diagonal elements (first sum in Eq. 4) were considered, and in a second run also the three off diagonal values (in this case 0.0) were included. In the first run, 11.4 ns were simulated, with the MD parameters set as given in Table 2. To study the time behaviour of the pseudo-forces they were not scaled up exponentially as in the other two case studies below. The width parameter was set equal to the experimental error as ΔP = 1 kHz, and the memory time was τ = 200 ps. The time development of the simulated tensor components for the first nanosecond can be seen in Fig. 3. The trajectories are shown for the 2H2 site, which possesses an experimental splitting of 40.5 kHz. After an initial time period of about 250 ps, the final average coupling tensor components are reached and their values fluctuate only weakly. The residual fluctuations depend on four parameters: (i) the thermal fluctuations controlled by the coupling to the heat bath, (ii) the pseudo-energy, (iii) the width parameter ΔP given in Eq. 11, and (iv) the memory time τ of Eq. 6. In the simulated 2H-NMR spectra (Fig. 4) these prevailing small fluctuations translate into line broadening, and the resulting line shapes are indeed very similar to the experimentally observed spectrum (Hoff et al. 2005).Table 1Deuterium quadrupolar coupling tensorsGroupCzzQ/kHzCyyQ/kHzCxxQ/kHzSourceC(sp2)–2H193−102.29−90.71Benzene, naphthalene (Rinné and Depireux 1974)C(sp3)–2H175−87.5−87.5Acenaphthene bridge (Rinné and Depireux 1974)–CD358.33−29.17−29.17C(sp3)–2Haa The values of the quadrupolar coupling constant are scaled by 1/3 to take into account the free methyl rotationTable 2General parameters for the MD simulations with orientational constraintsParameterValueTarget temperature293 KMD time step0.5 fsCoupling time η to the heat bath0.05 psPseudo-force width ΔP (Eq. 11)1 kHzMemory decay time τ for the average (Eqs. 6, 7)200 psTime constant ρ for the exponential rise of pseudo-forces200 psTotal MD duration1 nsFig. 3Trajectory of the time development of the averaging for the deuterium tensor components for 2H2 of pyrene. Under the influence of orientational pseudo-forces all components approach their final values after a period of about 250 ps. The component at about 20 kHz is experimentally observable in an oriented sample with the membrane normal parallel to B0. The component at −10 kHz would correspond to an experiment with a perpendicular sample alignmentFig. 42H-NMR spectrum from the MD simulation of deuterated pyrene, using orientational constraints from an NMR measurement in oriented POPC membranes (Hoff et al. 2005)
In this first simulation we had constrained only the three principal components of the quadrupolar tensor. It turned out that in this case small off-diagonal values did not approach zero but remain in the range of −1.5 to 2 kHz up to the end of the simulation (see Fig. 3). At the beginning of the simulation a high pseudo-energy of 31,000 kJ/mol was encountered, which dropped to 0.8 kJ/mol by the end of the simulation. Because of the large pseudo-energies at the start, we realized that it is preferable to first run a preliminary MD simulation (1–4 ns) with much smaller pseudo-forces, and then to step up the pseudo- forces after some initial averaging has been performed. In the next two cases studies the pseudo-forces were switched on exponentially to avoid long preliminary equilibration periods.
In a second MD simulation of pyrene over 7.8 ns, we also constrained the three off-diagonal tensor components Cxy, Cxz, and Cyz to zero, which are averaged by rotations of the molecule about the membrane normal. The corresponding values calculated from the MD run yielded off-diagonal elements with absolute values smaller than 0.23 kHz. The pseudo-energy increased by a factor of two, as it now contains contributions from both the diagonal and off-diagonal tensor components. In an attempt to compensate for the higher pseudo-energy, we set the memory time to a higher value of τ = 1,000 ps (see Eq. 6). Altogether we obtained smaller fluctuations of the coupling tensor components and much narrower lines in simulated spectra, which can be most likely attributed to the damping effect of the memory function outweighing the higher pseudo-energies.
Table 3 summarizes the calculated quadrupolar splittings corresponding to the results of the second simulation, in which all tensor components were constrained. The values were averaged for any sites on pyrene with mutually parallel C–2H bond directions, so that the 2H1 splitting is a mean value of two and the others are mean values of four sites each. The MD simulation is seen to reflect the experimental 2H-NMR data very well in several aspects. First of all, the calculated quadrupolar splittings reproduce the experimental values very well. The marginally lower values found for the simulated splittings might be explained by a slightly elevated temperature encountered during the simulation. Namely, the mean temperature of this simulation was 302 K and the final temperature 299 K. Since the target temperature of the simulation was 290 K, the NMR constraints produced a constant temperature increase of about 10 K. This difference would explain the slightly smaller simulated splittings compared to the experiment. In full agreement with the experimental data, no significant difference was found for the sites 2H2 and 2H3. In the simulations, the difference between their splittings was within the scatter of the individual sites, and this difference could neither be resolved experimentally. Furthermore, also the averaging of the pyrene molecule in the membrane is reflected correctly in the simulation. The values for the tensor components perpendicular to the B0 field direction are negative and half of the values of Table 3, indicating that the simulation does indeed lead to an averaged axially symmetric tensor aligned parallel to the membrane normal.Table 3Calculated quadrupolar splittings in a 7.8 ns constrained MD simulation of pyrene, compared with the experimental values obtained from a 2H-NMR measurement of the deuterated pyrene in oriented POPC membranesSiteSign of tensor componentaConstrained MD splitting Δν (kHz)NMR experimentalb splitting Δ ν (kHz)2H1+91.4 (91.39, 91.4)c93.02H2+39.9 (39.8–40.0)c40.52H3+39.7 (39.6–39.8)c40.5a The sign is given for the orientation of the membrane normal in B0 field directionb Hoff et al. (2005)c Range of simulated values for two or four sites, respectively
The experimental 2H-NMR data only reflect dynamic averages of e.g. the C–2H bond orientation, but can access only little information on the dynamic processes leading to the observed splittings. Here, the constraint-driven MD simulation provides a way to obtain details on molecular motions that are compatible with the experimental data. In the example of pyrene, the symmetry of the molecule and its motions in the membrane allowed us to derive information on the dynamic behaviour of the molecule from its observed splittings. Since in our case the coupling tensors are diagonal within the bond coordinate system and the segmental order tensor is also diagonal, simple expressions for the quadrupolar splittings are obtained:
and for the case of an axially symmetric coupling tensor CQ we get
The latter expression is especially useful, since the quadrupolar splitting of a site A is directly related to a single segmental order parameter. Assuming that the order tensor is diagonal in the laboratory frame, Eq. 21 applies to dipolar splittings as well, since the static dipolar coupling tensor is always axially symmetric and traceless, and the same holds for a rapidly rotating CD3-group. However, since the coupling tensor CQ for the C(sp2)–2H bonds is not axially symmetric (see Table 1), the three principal elements of the order tensors WA contribute to the splitting as in Eq. 20. It is therefore not possible to extract the order parameter tensor components from a single experimental splitting. From the course of our MD simulation, on the other hand, all components of the orders tensors WA (Table 4) are available, and the terms of Eq. 20 can be readily analysed.Table 4Simulated order parameters for pyrene in POPCSiteWxxWyyWzz| Wxy,Wxz,Wyz|2H1+0.021−0.342+0.321≤0.032H2+0.181−0.310+0.129≤0.072H3+0.165−0.294+0.129≤0.03SbbSccSaa|Sab, Sac, Sbc|Moleculea+0.048−0.275+0.228≤0.006a The values for the molecular Saupe order tensor are ordered in the columns such that the axes of inertia fit to the corresponding coordinate axes of the 2H1 site (see Fig. 2)
For a perfectly rigid pyrene molecule the molecular Saupe order tensor S and the segmental order tensors WA are linked by fixed conformation tensors. With our choice of the molecular coordinate system, S and WA are identical for the site 2H1, and for the other two sites the tensors are related by simple geometric expressions. Comparing the molecular order tensor S (last row in Table 4) and the segmental order tensor W (e.g. of the site 2H1, which is expressed in the same coordinate system, first row in Table 4), it is noticed that most of the molecular S tensor components are slightly smaller than the corresponding values of the segmental order tensor W. This effect is caused by bond vibrations and molecular twists occurring during the MD simulation. However, as these contributions to the dynamics (manifested in the difference between segmental and molecular order) are small, the mobility of the pyrene in the membrane is well described by the molecular order tensor S. Further qualitative conclusions on the behaviour of the pyrene molecule in the membrane can thus be drawn from the order tensor. Because Saa constitutes the largest (signed value) component, it is obvious that the corresponding axis a (in the system of inertia), and with it the long molecular axis of pyrene, shows some preference for an orientation in the direction of the membrane normal. Since Scc and Sbb are different, the molecular motions are not axially symmetric, but display a preference for a second axis, expressed in a biaxiality parameter ξ = 0.11 (see Eq. 16). This pronounced biaxiality is a consequence of the restricted motions of the flat pyrene molecule within the lipid matrix. A further indication for such a restricted motion is the fact that the smallest component of the order tensor S is found along the c-axis direction perpendicular to the aromatic ring system. This direction has thus the smallest average component  along the membrane normal and is most conserved, as expected for the planar shape of the molecule. In their all-atom MD simulation including 128 lipid molecules and water, Hoff et al. (2005) obtained essentially the same results as in this work, with the exception that they found a much higher Saupe order component Szz = 0.42 (in this work denoted with Scc).
Cholesterol
Cholesterol is a constituent of many biomembranes, and its orientation and motion within the lipid bilayer is of great interest to explain its influence on membrane properties. The behaviour of cholesterol in membranes has been studied extensively, especially by 2H-NMR in view of its sensitivity towards molecular alignment and dynamics (Smith and Oldfield 1984). We have based our MD analysis on a recent study by Marsan et al. (1999), who had measured and interpreted 2H-NMR spectra of partially deuterated cholesterol in oriented DMPC (dimyristoylphosphatidylcholine) bilayers. The labelled sites in the sterol ring system are indicated in Fig. 5.Fig. 5Cholesterol molecule with 2H-labelled sites indicated, for which experimental orientational constraints were available. From the MD simulation quadrupolar splittings were calculated for all deuterons in the ring system
The authors examined six different models of cholesterol structures proposed in the literature using an RMSD analysis to assign all observed lines in the 2H-NMR spectrum. In this analysis the quadrupolar splittings were calculated from the respective rigid molecular models, treating the molecular orientation and the Saupe tensor as free variables. It turned out that the results depended crucially on the selection of the molecular coordinates, and only the data from a neutron diffraction analysis (McMullan et al. 1992) of a cholesterol derivative (20-CH3-methylpregnene-3,20-diol) produced convincing results. This strong dependence on the exact coordinates is regarded as one of the main drawbacks of RMSD analyses of rigid molecular models.
In the analysis presented here, we performed MD simulations for both methylpregnenediol and cholesterol, having modelled the latter with COSMOS (see Fig. 5). As constraints for the MD simulations we used the 2H-NMR splittings of the eight experimentally observed deuterium labels of Marsan et al. (1999), together with their best assignment (see Table 5). In addition, from the MD simulations we predicted the putative quadrupolar splittings of all other protonated sites of the sterol ring system. This new data from our MD simulations can thus be used in the future to assign further 2H-NMR spectra without the need for any special labelling schemes or particular molecular coordinates. As outlined above, we again used the full quadrupolar tensors as constraints. Two short preliminary MD runs were performed to determine the signs of the coupling constants, since this information is required for the full tensorial constraints. All quadrupolar splittings of cholesterol which had been measured in oriented samples with the membrane normal parallel to B0 yielded negative coupling constants, (giving correspondingly positive values for the perpendicular orientation, see Table 5). As in the example of pyrene, MD simulations over 1,000 ps were sufficient to reproduce all features of molecular motion. To avoid too large pseudo-forces at the start of the simulation, the pseudo-forces were exponentially increased to their final values during the MD run. The parameters used in the simulations are summarized in Table 2.Table 5Calculated deuterium quadrupolar splittings from a constrained MD simulation compared with the experimental 2H-NMR data (Marsan et al. 1999)SiteConstrained MD splitting of methyl-pregnenediol (kHz)Constrained MD splitting of cholesterol (kHz)Experimental NMR splitting (kHz)H2a−101.5−101.4101.68H2e−66.7−67.767.86H3_1−107.1−106.7107.30H4e−63.2−62.362.68H4a−94.3−93.094.98H6−7.29−6.696.44H7a−92.9−95.496.12H7e−91.6−91.391.48C18–CD3−33.0−33.8C19–CD3−37.5−34.7H1a−104.2−99.2H1e−69.4−46.1H8_1−107.0−100.3H9_1−112.6−103.0H11a−106.4−102.7H11e−50.7−74.7H12a−46.4−13.1H12e−103.1−101.1H14_1−103.4−100.3H15_1−102.1−81.8H15_2−93.5−88.9H16_1−17.6−40.9H16_2−23.1−27.6H17_1−85.7−92.7
The final results for the quadrupolar splittings obtained from the constrained MD simulations of both sterols are summarized in Table 5. The corresponding simulated 2H-NMR spectrum of cholesterol is shown in Fig. 6, indicating the peak positions of all ring deuterons. This presentation can be helpful in the future for assigning the signals of fully deuterated cholesterol derivatives. Overall, the theoretical and experimental values agree very well within 1 kHz (except for 2H6). The only noteworthy deviation is seen for 2H7a, where the calculated splitting of methylpregnenediol differs by 3.2 kHz from the experiment. Overall, the simulated cholesterol quadrupolar couplings agree slightly better with the experimental values than the respective methylpregnenediol values. This finding is also reflected in the lower mean temperature reached in the simulation of cholesterol (323 K) than for methylpregnenediol (336 K). The higher mean temperature of the latter is probably also responsible for the slight differences between the calculated splittings of the two molecules in some of the other sites, too (see Table 5).Fig. 6Calculated 2H-NMR spectrum from the MD simulation of deuterated cholesterol in an oriented membrane, with the membrane normal parallel to B0. Pseudo-forces were applied in the MD simulation to a sub-set of deuterons for which experimental data was available (see Fig. 5). The peak positions of all other deuterons connected to the ring skeleton are also predicted and indicated (see Table 5). The intensity of the CD3-signals has to be multiplied by a factor of three
The constrained MD simulation showed that both cholesterol derivatives essentially assume an upright orientation in the membrane and perform rotations mainly around their long molecular a-axis of inertia. This behaviour is reflected in the molecular Saupe order tensor (see Table 6), whose values are not far from the ideal axially symmetric case of Eq. 16 with a low biaxiality of ξ = 0.01. Previous calculations of the order tensor from the NMR data (Marsan et al. 1999) had given similar results as the present MD simulation. We find that the preferred orientation of the long a-axis of the molecule displays a small mean tilt angle Θ a of 13° with respect to the membrane normal. A similar result was obtained by Marsan et al. (1999), who reported a tilt of the long molecular axis (as defined in the paper of Dufourc et al. 1984) with respect to the membrane normal of 12° (provided that in their RMSD data analysis the restriction to a rotationally symmetric order tensor was dropped).Table 6Molecular Saupe order tensor derived from the constrained MD simulation, compared with the values derived from the previous RMSD analysis (Marsan et al. 1999)Tensor componentMD simulation of methylpregnenediolMD simulation of cholesterolStatic RMSD analysisSaa0.870.880.94Sbb−0.44−0.44−0.48Scc−0.43−0.44−0.46Sab0.0−0.11Sac0.0−0.12Sbc0.00.0
We note that the axially symmetric averaging of cholesterol does not imply that its motion resembles a smooth rotation about a fixed axis. In the MD simulation we observed frequent jumps of the long axis by 20 to 30°, accompanied by a change of the conformation of the aliphatic tail of the molecule. This behaviour can be examined more closely by inspecting the trajectory of the angle Θ a between the a-axis of inertia and the membrane normal (z-axis, see Fig. 7). Under the influence of the full pseudo-forces after 150 ps, the long axis of cholesterol frequently jumps off the most preferred orientation of Θ a = 4°. As a consequence, the mean orientation and the most frequent orientation do not coincide. Methylpregnenediol behaves clearly different in the MD simulation, since it has only a short aliphatic tail that does not give rise to conformational changes.Fig. 7Trajectory of the time development of the angle Θa between the a-axis of inertia and the z-direction (membrane normal). The axis frequently jumps off its most preferred orientation due to conformational changes in the aliphatic tail of the molecule, hence the mean tilt (13°) and the most preferred tilt (4°) do not coincide
PGLa
In our third case study, the new MD approach was applied to a membrane bound peptide. PGLa is a 21-residue cationic peptide (GMASKAGAIAGKIAKVALKAL-NH2) from the magainin family of antibiotics present in frog skin, which folds into an amphiphilic α-helix when bound to lipid bilayers. The mechanism of antimicrobial activity has been addressed in numerous studies and is attributed to the perturbation of bacterial membranes. Solid state NMR has yielded much insight into its structure and dynamic behaviour in model membranes (Bechinger 1999). For our MD simulations we used the constraints from the 2H-NMR investigations of Strandberg et al. (2005), who had labelled four native alanine residues (positions 6, 8, 10 and 14) and two isoleucines (positions 9 and 13) one by one with 2H3-alanine. At a peptide-to-lipid ratio of 1:200 in DMPC, it was found from the quadrupolar splittings of these six CD3-groups that the α-helical PGLa is aligned flat in the plane of the membrane, and the peptide undergoes fast rotational diffusion about the membrane normal at 35°C. From the same data we now derived the full quadrupolar coupling tensors for the six 2H-labelled sites, including the sign of the tensor elements, and used these as constraints for the MD simulations. For the quadrupolar coupling tensors we used the parameters of the CD3-group (Table 1), both for the native Ala substitutions as well as for the nominal constraint along the Cα–Cβ segment of isoleucine.
It is known from previous NMR studies (Strandberg et al. 2005) that membrane-bound PGLa forms an α-helix in the range of the labelled stretch, and further evidence for an α-helical conformation between residues 6 and 21 is provided by (Bechinger 1999). We therefore started the MD simulations with an idealized α-helix, and for all backbone hydrogen bonds we introduced 18 additional distance constraints of 1.86 Å to keep the molecular model helical. At the end of the simulation, the RMSD of the backbone hydrogen bond lengths from their ideal values was only 0.2 Å, thus confirming that that the molecule stayed indeed mostly helical during the MD simulation. It also had to be taken into account that the four lysine side chains and N-terminus are positively charged. Since charged NH3+ groups have a strong tendency to form hydrogen bonds, we added a water molecule near each hydrogen atom of a charged group. This way any undesired hydrogen bonds of lysines to the backbone could be prevented, and indeed at the end of the simulation all 15 water molecules were still in contact with the peptide.
Compared to the two small-molecule case studies above, the NMR data on PGLa provide only few constraints on the large and rather flexible molecule, as the pseudo-forces act only on six amino acids out of 21. Therefore, unrealistically large-pseudo forces at the start of the MD simulation have to be avoided. As a first measure, the pseudo-forces were increased exponentially to their final values during the MD simulation with a time constant of ρ = 100 ps, like in the cholesterol simulation. From the NMR studies it was known that PGLa rotates rapidly (on the NMR time scale) about the membrane normal. This motion opens up a second possibility to scale the pseudo-forces properly. We calculated rotational averages of the quadrupolar tensors in every step of the MD. Only three tensor values rotated by 120° around the director axis (in this case the membrane normal oriented parallel to the B0 field directions) are necessary to obtain a mean tensor within the limits of a fast rotation. All off-diagonal values of the simulated tensors thus become zero and only the principal values of the tensors are left as constraints. The mean temperature at the end of the simulation was only 11 K above the target temperature of 293 K, indicating that exceedingly high pseudo-forces have been successfully avoided indeed, which would otherwise have lead to additional heat production.
As in the other two case studies above, the constraint-driven MD simulations were able to reproduce the experimental 2H-NMR data correctly, as evident from the comparison in Table 7. All calculated frequencies are well within the experimental error margins (as estimated from the experimental line widths). For some additional sites the quadrupolar frequencies were predicted from the MD simulation, and it would be of interest whether future measurements can confirm these values.Table 7Calculated deuterium quadrupolar splittings from a constrained MD simulation of PGLa, compared with the experimental 2H-NMR data (Strandberg et al. 2005) in DMPCSiteMD (kHz)NMR (kHz)Ala3−22.2Ala6+15.7+15.6Ala8+17.1+17.2Ala10−15.0−15.0Ala14−26.8−26.6Ala17+17.1Ala20−25.5Ile9−4.9−5.2Ile13+26.226.4
The major aim of the present NMR data analysis was the determination of the time- and ensemble averaged alignment of the helical PGLa backbone in the membrane environment. As illustrated in Fig. 8, the principal axes of inertia offer a suitable molecule-fixed frame of reference to describe the peptide orientation. In the PGLa the a-axis of least inertia points along the helix, and the c-axis of highest inertia is located between the hydrophilic lysine-rich face and the hydrophobic face of the molecule formed by alanines, valines, leucines and isoleucines. Intuitively, we would expect the helix to be in contact with biomembranes just like it is displayed in this view, namely with the intermediate b-axis collinear with the membrane normal.Fig. 8View along the long axis of PGLa modelled as an idealized α-helix with only the lysine, alanine and isoleucine side chains displayed. The latter two types were labelled by 2H3-alanine, and their 2H-NMR splittings were included into the MD simulation. The principal a-axis of inertia, points along the helix, and the b-axis of inertia points radially through Cα of lysine 12
The outcome of the MD simulation is illustrated most appropriately in Fig. 9, which visualizes the alignment and dynamic behaviour of PGLa in DMPC. Here, the peptide orientations during the MD simulation are presented as a scatter plot of the inertia axes a, b and c on the surface of a unit sphere. A narrow range of orientations is seen for the a-axis representing the peptide helix. In contrast, the broad scatter for b and c indicates that a high degree of motional freedom exists for motions around the helix axis. The time-averaged tilt angle of the a-axis with respect to the membrane normal in found to be 99°, with a mean deviation of about ±4°. This value is in very good agreement with the tilt angle τ ≈ 98° of PGLa that had been deduced from an RMSD plot of the 2H-NMR by Strandberg et al. (2005) assuming a rigid idealized α-helix. We note, however, that such helix axis definition is not exactly comparable to the a-axis of inertia used in our MD calculations. The authors further described the azimuthal alignment of the amphiphilic peptide by defining an angle ρ between an axis pointing from the centre of the helix radially through the Cα-atom of lysine 12 and the membrane plane. In our case, the b-axis of inertia also happens to be directed to Cα of lysine 12 (see Fig. 8), but the centre of inertia does not exactly coincide with the helix axis. As can be seen from Fig. 10, the angle Θ b between the b-axis and the membrane normal oscillates between 10° and 70°, with a mean value of 33°. This average value corresponds to an angle ρ of approximately 123°, which compares well with ρ ≈ 115° extracted from the previous analysis (Strandberg et al. 2005). In this comparison we have to keep in mind that in our simulations the PGLa molecule was found to be far from rigid and possesses a high degree of mobility around the helix long axis. The lysine side chains are found to spread out much more than in the initial model, and the PGLa helix itself exhibits a small bend.Fig. 9Visualization of the alignment and motional behaviour of the α-helical peptide PGLa (only lysine side chains shown) in a DMPC membrane, as determined by a 1 ns MD simulation using experimental 2H-NMR constraints (Strandberg et al. 2005) The instantaneous orientation of the three axes of inertia a, b and c is displayed as a scatter plot on a sphere, representing snapshots of these axes orientations every picosecond. The poles of the sphere are oriented parallel to the membrane normal. The helix long axis displays only a small scatter with a mean tilt angle of 99°, while the two other axes oscillate about this direction producing a large scatterFig. 10Molecular dynamics trajectory of the angle Θb between the intermediate b-axis of inertia and the membrane normal
Similarly important as the alignment of the peptide are its dynamic properties, which also enter into the observed 2H-NMR data. Table 8 lists the components of the molecular Saupe tensor, which is again used to describe the average alignment a molecule fixed frame (i.e. the principal axes of inertia) with respect to the laboratory frame. However, in contrast to the two previous case studies, this Saupe tensor does not represents the fast reorientation of the molecule around the membrane normal. No pseudo-forces were driving this motion in the simulation, since a rotational mean value was introduced. Here, the order tensor represents only the wobble of the long a-axis and the pronounced oscillations about this molecular axis (see Fig. 10). As expected from the distribution of polar and hydrophobic residues on the different faces of the helix, it is seen that the Saupe tensor is not diagonal Table 8). The biaxiality of ξ = 0.09 (S = 0.76, see Eq. 15) is clear evidence that the molecule does not behave like a rod-shaped object, and rotation about its long axis is restricted. In fact, oscillations of about 60° around the long axis, but no complete rotation events were observed in the MD simulation (see Fig. 10). These oscillations start after about 100 ps when the pseudo-forces approach their full strength.Table 8Molecular Saupe order tensor from a constrained MD simulation of PGLaTensor componentMDSaa+0.49Sbb−0.28Scc−0.46Sab−0.46Sac+0.06Sbc−0.08
At a first glance the rather regular oscillations of PGLa in Fig. 10, or the rotations of cholesterol (Fig. 7), may not seem to reflect reality. However, we have to keep in mind that our MD simulations are performed with single molecules in vacuum, and the temperature is controlled by a continuously acting NTV “thermostat” adjustment. In nature, heat is transferred by stochastic interactions with other molecules, thereby introducing a stochastic behaviour of molecular rotations and re-orientations. This stochastic aspect is not present in our MD simulations, but nonetheless the amplitudes, velocities and directions of the motions adopt realistic values as the NMR constraints have to be satisfied. The molecule in the simulation cannot stop in one preferred orientation, because then no averaging would be performed anymore.
Conclusions
Solid state NMR is a valuable technique to gain insight into the behaviour of peptides and proteins in oriented media, provided the data can be interpreted in terms of molecular structure and dynamics. In this contribution we developed a new strategy in which all-atom MD simulations and NMR data obtained from oriented samples are combined to obtain such structural and motional information. To this aim, a molecular mechanics force field (in this case COSMOS-NMR) was extended to include pseudo-forces, which drive the molecular dynamics to meet the NMR constraints. They “heat up” molecular rotations or re-orientations, leading to proper averaging of the calculated tensor values such that the calculated tensor values agree with the corresponding experimental observations. The orientational constraints can be further combined with intramolecular constraints such as distances or chemical shifts. This way, similar results can be obtained as in full membrane MD simulations, but without the computational burden of having to perform a detailed simulation of the lipids and surrounding water molecules. Because they are performed in vacuum, the constrained MD simulations can be completed in relatively short simulation times (≤1 ns), still reaching a complete averaging of the NMR observables.
In three case studies, ranging from a small rigid compound to a 21-residue membrane-active peptide, the MD simulations with orientational NMR constraints succeeded to produce a detailed picture of the molecular motions and orientations in oriented membranes. It could be demonstrated that this new method is not limited to rigid molecules and does not depend on the choice of the initial coordinates. Deuterium quadrupolar splittings from 2H-labelled pyrene, cholesterol and PGLa in oriented lipid bilayers have been used as constraints in the present examples, but the general formalism presented will be applicable to all kinds of tensorial NMR properties.

molecular dynamics simulations
pyrene
cholesterol
order parameters
2h-nmr
oriented samples
orientational nmr constraints
pgla peptide
force field calculations


Long-term cognitive and behavioral consequences of neonatal encephalopathy following perinatal asphyxia: a review

Neonatal encephalopathy (NE) following perinatal asphyxia (PA) is considered an important cause of later neurodevelopmental impairment in infants born at term. This review discusses long-term consequences for general cognitive functioning, educational achievement, neuropsychological functioning and behavior. In all areas reviewed, the outcome of children with mild NE is consistently positive and the outcome of children with severe NE consistently negative. However, children with moderate NE form a more heterogeneous group with respect to outcome. On average, intelligence scores are below those of children with mild NE and age-matched peers, but within the normal range. With respect to educational achievement, difficulties have been found in the domains reading, spelling and arithmetic/mathematics. So far, studies of neuropsychological functioning have yielded ambiguous results in children with moderate NE. A few studies suggest elevated rates of hyperactivity in children with moderate NE and autism in children with moderate and severe NE. Conclusion: Behavioral monitoring is required for all children with NE. In addition, systematic, detailed neuropsychological examination is needed especially for children with moderate NE.
Introduction
It has been widely accepted that severe NE following PA can lead to serious motor disabilities, mental retardation and seizure disorders [59]. Traditionally, research in this area has focused on developmental consequences at a very young age. In addition, the effects of NE have often been treated as an all-or-nothing phenomenon; patients have been believed to show a completely normal development or become severely disabled. So far, few studies have looked at milder consequences of NE at school age.
From perinatal asphyxia to neonatal encephalopathy
PA can be defined as impaired respiratory gas exchange accompanied by the development of acidosis [30]. The pathway of PA can have its onset in the antepartum, intrapartum or postpartum period. Estimates of the incidence of PA vary from 1 to 8 per 1,000 live births [4]. This wide range can be largely attributed to problems in selecting indicators to identify children with PA. As a consequence, researchers have used many different sets of inclusion criteria when studying the effects of PA. For this reason, it is difficult to compare results regarding developmental outcome of infants with PA across studies.
In the past, single-marker definitions were commonly used to diagnose infants with PA (e.g., low Apgar score). However, these single markers have relatively low sensitivity and specificity for accurate diagnosis [54]. Moreover, they lack predictive power [3, 20, 35–37, 40, 51]. Therefore, a shift has been made towards multiple-marker definitions based on combinations of indicators [16, 35]. Among the perinatal indicators are fetal distress (abnormal heart rate or meconium-stained amniotic fluid), delay in onset of spontaneous respiration, low Apgar score (<6 or 7 at 5 min), need for resuscitation and/or ventilation, and metabolic acidosis (cord pH <7.0 or 7.1 and base deficit >12 mmol/l). Postnatal indicators of PA include NE, multi-organ failure and abnormal findings on brain imaging. Of the above, the concept of NE is generally accepted as an essential criterion for the diagnosis of PA [4, 30]. NE has been defined as a clinical syndrome of disturbed neurological function in the earliest days of life in term infants, manifested by difficulty with initiating and maintaining respiration, depression of tone and reflexes, subnormal level of consciousness and often by seizures [30].
Recent studies of the protective effects of head cooling and systemic hypothermia after NE following PA used a combination of clinical and physiological criteria of PA (including a pH of 7.0 or less or a base deficit of 16 mmol per liter or more) and a neurological examination to determine the severity of NE [22, 52]. These inclusion criteria are closely related to a set of criteria postulated by The American College of Obstetricians and Gynecologists (ACOG). There has been extensive debate about the relationships between PA, NE and serious possible consequences such as CP [25]. Contrary to what was once believed, only a small part of all cases of CP are caused by intrapartum events [10]. Likewise, many cases of NE are not associated with a history of PA [6, 38]. However, NE has to be present in the pathway from PA to subsequent CP [25]. The ACOG and the International Cerebral Palsy Task Force proposed a set of criteria to define an acute intrapartum hypoxic event that could subsequently lead to CP [4, 30]. The presence of moderate or severe NE and metabolic acidosis (pH <7 and base deficit ≥12 mmol/l) are considered to be essential markers for hypoxic ischemic injury. In addition, these criteria underline the importance of excluding other identifiable causes of NE before assuming asphyxia as the underlying etiology.
Both in clinical practice and for scientific purposes, patients suffering from NE are often assigned an encephalopathy score. Several classification systems can be identified. Leviton and Nelson provide an overview of qualitative and quantitative grading schemes [27]. In addition, scoring systems were developed that are applicable in low-income settings. Moreover, a new grading system has gained interest in the literature [19, 33]. The classification system of Sarnat and Sarnat developed in 1976 [50] is still widely used and the basis for most modern classification systems. In this review, the Sarnat system is used to grade NE because in most studies that were included this system it had been applied. According to the Sarnat system children are assigned a score of 1, 2 or 3 (1= mild NE; 2= moderate NE; 3= severe NE). Stage 1 lasts less than 24 h and is characterized by hyperalertness, uninhibited Moro and stretch reflexes, sympathetic effects and a normal EEG. Stage 2 is marked by obtundation, hypotonia, strong distal flexion and multifocal seizures. Infants with the most severe stage of NE (stage 3) are stuporous and flaccid, and brain stem and autonomic functions are suppressed. In addition, the EEG shows abnormal patterns of brain activity.
Grades of neonatal encephalopathy in relation to developmental outcome
Encephalopathy scores have often been used to predict neurodevelopmental outcome [43, 44, 46]. Although strong relationships exist between the categories of NE and developmental outcome, diversity of outcome is reported within individual grades [41]. This diversity may be partly explained by individual researchers using different neurological signs for category inclusion [27]. The Sarnat grading system has helped to clarify the NE categories and improved their usefulness in studies of prognosis [45].
Most outcome studies have focused on neurological functioning and severe deficits in young children (<4 years). In general, very few children with mild encephalopathy show neurological impairments or have developed severe mental or motor retardation at preschool age. In contrast, children with severe encephalopathy nearly always die or develop severe impairments such as CP, mental retardation, epilepsy and in some cases sensorineural hearing loss or cortical visual impairment [4, 45, 47]. While dyskinetic CP or quadriplegia is more commonly seen following “acute near total type of asphyxia” with MRI abnormalities present in the deep grey nuclei and in the perirolandic cortex, milder forms of CP, such as spastic diplegia, are more common among those with “subacute partial asphyxia” with MRI abnormalities in the watershed areas. Hemiplegia can be seen following “perinatal arterial stroke.” Although this condition will present with NE and seizures, it is less commonly associated with PA [13, 34]. Children who have suffered moderate encephalopathy seem to form a more heterogeneous group; research shows varying rates of infant death and morbidity [16].
Need for long-term follow-up and focus on cognitive and behavioral outcomes
Because the majority of studies have focused on detecting major developmental abnormalities at a very young age, still little is known about the milder difficulties children may experience in the long run. Recently, however, research has started to focus more on the subtle problems of children without major neurological deficits [8, 23, 32, 35, 43, 44, 46]. Knowledge of the outcome for this group of children is important because these subtle problems can affect later scholastic achievement and behavioral adjustment.
Cognitive and behavioral difficulties can be expected because of the patterns of brain injury that have been associated with NE. The hippocampus and striatum are among the brain structures that can be affected [7, 21, 31, 42, 56]. These structures have been associated with specific cognitive functions such as memory and attention and hypothesized to play a role in the pathogenesis of ADHD, autism and schizophrenia [14–16, 28, 57].
To gain insight into the long-term effects of NE on development, follow-up is required throughout the school-age period. Specific cognitive functions continue to develop throughout childhood. As it is impossible to examine a function that has not developed yet, short-term follow-up cannot exclude minor cognitive and behavioral difficulties at school age. Moreover, subtle functional deficits usually do not become apparent until a child faces increasing demands to master complex abilities in school.
The aim of this review was to discuss the results of previously published studies on the possible effects of NE on general cognitive functioning, educational achievement, neuropsychological functioning and behavior from infancy through adolescence. A literature search was performed using Web of Science. Studies were screened that had been published before October 2006. Search terms were ’asphyxia neonatorum,’ ‘neonatal encephalopathy,’ ‘newborn encephalopathy,’ ‘hypoxic-ischemic encephalopathy’ and ‘perinatal asphyxia’ combined with the terms ‘cognition/cognitive’ and ‘behavior(-al)’. Reference lists of retrieved articles were screened for additional relevant publications. Studies were selected that reported the developmental outcome of NE or HIE following PA of children until the age of 18 years. The presence of NE was required for selection. When no additional indicators of PA were used, the asphyxial etiology of the NE had to be assumed by the authors. Studies were excluded when NE was partially caused by other conditions than PA, for example [17, 34]. Also, studies were excluded in which outcome was presented in preset categories (e.g., normal versus abnormal), in which several outcome measures were combined, for example [11, 18, 26]. In these cases it was not possible to judge whether a child had been assigned to the category ‘abnormal’ because of cognitive or behavioral problems or because of other problems, e.g., motor difficulties. A third reason for excluding studies from this review was the selection of children from a larger group of eligible patients. For example, some researchers selected only those children with abnormal findings on MRI in order to examine the correlation with outcome [61]. In this review, outcome is reported according to grade of NE (mild, moderate or severe). Most authors of the included studies used the classification system of Sarnat when determining the severity of the NE. However, in three cases the authors significantly deviated from this system [32, 47, 53]. In order to increase comparability across studies, the patients in these studies were reclassified according to Sarnat criteria. Articles were reviewed that reported general intellectual ability, levels of specific cognitive functions and/or behavioral problems. Details of the samples, inclusion criteria, outcome measures and results are described in Tables 1, 2, 3, 4, 5, 6, 7 and 8.
Table 1Griffiths Mental Development Scale* at 1 year CPNo CPCarli et al. (2004) [12]Moderate NE, N=13Moderate NE, N=27• N=10: <−2 SD• N=4: between −2 SD and −1 SD• N=1: between −2 SD and −1 SD• N=23: >−1 SD• N=1: >−1 SD• N=1: untestableGray et al. (1993) [24]Moderate NE, N=10Moderate NE, N=9• N=9: <55Mean =118 (N=1 not assessed)• N=1: 87Thompson et al. (1997) [55]No grades of NE recorded, N=16No grades of NE recorded, N=24Mean =47, range: 7–101Mean =16, range 94–128Barnett et al. (2004) [9]No grades of NE recorded, N=59Mean =100.34, SD=15.05, range: 55–130Rutherford et al. (1996) [49]Moderate NE, N=8Mild NE, N=4All below normal (cut-off point not reported)All normalSevere NE, N=1Below normal*Griffiths mean of standardization sample =100.18, SD=12.76Table 2IQ at 2–6 years (no CP) Age (years)Mild NEModerate NESevere NEBarnett et al. (2004)b [9]2N=45,f Griffiths: mean =103.53, SD=12.33 (range: 81–140)Robertson & Finer (1985)a [43] 3.5N=66, Stanford-Binet: mean IQ =101.5, SD=14.0N=94 (21.3% handicapped) Stanford-Binet2: mean IQ =92.3, SD=23.2N=7, Stanford-Binet2: mean IQ =37.1, SD=26.7Shankaran (1991)cb [53]5N=14,f McCarthy: 64%: >−1 SD, 86%: >−2 SDBarnett et al. (2002)bd [8] 5.5–6.5N=20, WPPSI-R: mean IQ =109.7, SD=14.6 (N=1: 76, rest: >90)N=12, WPPSI-R: (N=1: untestable) N=11: mean IQ =106.2, SD=11.8 (N=1: 84, rest: >94)Barnett et al. (2004)b [9]5–6N=53,f WPPSI-R 85%: >−1 SD, mean IQ =101.98, SD=16.06, range: 69–139Robertson & Finer (1988)e [44]5.5 N=56, Stanford-Binet: mean IQ =106, SD=12N=71, Stanford-Binet: mean IQ =99, SD=18)Griffiths Mental Developmental Scales: mean of standardization sample =100.18, SD=12.76; Stanford-Binet Intelligence Scales: mean of standardization sample =100, SD=16; McCarthy Scales of Children’s Abilities: mean of standardization sample =100, SD=16; Wechsler Preschool and Primary Scale of Intelligence-revised: mean of standardization sample =100, SD=15. aAll groups are significantly different from the other groups; p=0.001. bNo significance tests performed. cPatients were reclassified by author (MH) according to the classification system of Sarnat [50]. dIn the original article, results are presented for individual cases. Group means and standard deviations have been calculated by one of the authors (MH). eMild NE not significantly below comparison group; moderate NE significantly below mild NE and comparison group. fCombined group of mild, moderate and severe NETable 3IQ at 7–9 years (no CP) Age (years)Mild NEModerate NESevere NEMarlow (2005)b [32]7N=65, BAS-II:Group 1 (N=34): GCS=112.3, SD=11.2Group 2d (N=31): GCS=102.7, SD=13.2Robertson et al. (1989)a [46]8N=56, WISC-R: mean IQ =106, SD=13N=66, WISC-R: mean IQ =102, SD=175, WISC-R: mean IQ =36, SD=7Robertson (1997)c [47]9N=64, WISC-R: mean IQ =100, SD=14WISC-R: Wechsler Intelligence Scale for Children-revised: mean of standardization sample =100, SD=15; BAS-II: British Ability Scales, GCS: General Cognitive Score: mean of standardization sample =100, SD=15. aIQ of mild NE is not significantly lower than IQ of comparison group. IQ of moderate and severe NE is significantly lower than IQ of mild NE and comparison group (p < 0.001). bGSC of group 1 is not significantly lower than GSC of comparison group. GCS of group 2 is significantly lower than GCS of comparison group (p < 0.01). cMean IQ is significantly lower than IQ of comparison group. dMarlow refers to this group as ‘severe NE.’ This group is reclassified by one fo the authors (MH) according to the classification system of Sarnat [50]Table 4Scholastic abilities at 7–13 years (no CP) Mild NEModerate NESevere NEReadingRobertson & Finer (1989) [46]13% >1 grade level below expected level for age35% >1 grade level below expected level for agea100% >1 grade level below expected level for agebRobertson (1997) [47]41% >1 grade level below expected level for agedMarlow et al. (2005) [32]Group 1: < control groupcGroup 2f: < control groupdMoster et al. (2002) [35]NE < control groupdNE < control groupdNE < control groupdWritingMarlow et al. (2005) [32]Group 1: = control groupGroup 2f: < control groupeMoster et al. (2002) [35] NE = control groupNE = control groupNE = control groupSpellingRobertson & Finer (1989) [46]2% >1 grade level below expected level for age18% >1 grade level below expected level for agea100% >1 grade level below expected level for agebMarlow et al. (2005) [32]Group 1: < control groupcGroup 2f: < control groupdMoster et al. (2002) [35]NE = control groupNE = control groupNE = control groupMathRobertson & Finer (1989) [46]16% >1 grade level below expected level for age20% >1 grade level below expected level for agea100% >1 grade level below expected level for agebRobertson (1997) [47]39% >1 grade level below expected level for agedMarlow et al. (2005) [32]Group 1:  = control groupGroup 2f: < control groupdMoster et al. (2002) [35]NE < control groupcNE < control groupcNE < control groupcRobertson & Finer (1989) [46]: age 8 years; Marlow et al. (2005) [32]: age 7 years; Moster et al. (2002) [35]: age 8–13 yearsaSignificant difference with mild NE and comparison group, p < 0.01; bstatistical analyses are not given because of small group and low scores; csignificant difference with comparison group, p < 0.05; dsignificant difference with comparison group, p < 0.001; esignificant difference with comparison group, p < 0.01; fMarlow refers to this group as ‘severe NE’. This group is reclassified by one of the authors (MH) according to the classification system of Sarnat [50]Table 5Neuropsychological functions [43, 44, 46]Tests3.5 years5.5 years8 yearsReceptive vocabularyMild NE: in the mean rangeMild NE = control groupMild NE = control groupModerate NE < mild NEaModerate NE< mild NE+ control groupbModerate NE< mild NE+ control groupaSevere NE < mild + moderate NEaSevere NE: 100% < −1 SD cSevere NE < mild NE + control group cVisual- motor integrationMild NE: in the mean rangeMild NE = control groupMild NE = control groupModerate NE < mild NEaModerate NE < mild NE + control groupaModerate NE < mild NE + control groupa Severe NE < mild NE + control group cSevere NE < mild + moderate NEaSevere NE: 100% < −1 SD cReceptive vocabulary: Peabody Picture Vocabulary Test, visual-motor integration: Developmental Test of Visual-Motor Integration. ap < 0.001; bp < 0.01; cstatistical analyses are not given because of small group and low scoresTable 6Neuropsychological functions (NEPSY): moderate NE compared to control group [32]Age: 7 yearsModerate NE group 1Moderate NE group 2*Attention and executiveGroup 1 = control group (p=0.08)Group 2 < control group (p < 0.01)LanguageGroup 1 < control group (p=0.01)Group 2 < control group (p < 0.01)SensorimotorGroup 1 < control group (p=0.04)Group 2 = control group (p=0.10)VisuospatialGroup 1 = control group (p=0.14)Group 2 < control group (p=0.02)Memory and learningGroup 1 = control group (p=0.61)Group 2 < control group (p < 0.01)*Marlow refers to this group as ‘severe NE.’ This group is reclassified by one of the authors (MH) according to the classification system of Sarnat [50]Table 7Verbal memory (Rey’s Auditory Verbal Learning Test)* [31]Age: 16 yearsModerate NETotal words learned trial 1–5NE: 52.5, SD=8.7Control group: 57, SD=5.8Delayed recall (p=0.034)NE: 11.6, SD=2.2Control group: 13.3, SD=1.6Recognition (p=0.011)NE: 14.2, SD=1.0Control group: 15, SD=0.0*Raw scores (number correct)Table 8Verbal memory (Children’s auditory verbal learning test)* [47]Age: 9 yearsModerate NELevel of learningaNE: 98, SD=16Control group: 104, SD=15Immediate recallaNE: 96, SD=18Control group: 105, SD=15Delayed recallaNE: 95, SD=15Control group: 103, SD=16Recognition accuracyNE: 22% delayedbControl group: 27% delayedTotal intrusionsNE: 19% delayedbControl group: 20% delayedImmediate attentionNE: 97, SD=15Control group: 100, SD=15*Mean of standardization sample =100, SD=15, ap < 0.01; bDelayed is below the 17th percentile of the standardization sample
General cognitive development
Infancy (0–24 months)
Eight studies were identified that measured developmental levels between the ages of 12 and 24 months in children with NE [2, 9, 12, 24, 39, 49, 50, 55]. Two of these were performed in low-income, non-western countries [2, 55]. Standardized testing with the Griffiths Mental Development Scales showed that almost all children with moderate NE who had been diagnosed to have CP at the age of 12 months were severely developmentally delayed (Table 1). In these studies, the rates of CP among children with moderate NE varied between 23% [24] and 82% [49]. Children with moderate NE without CP at this age performed in the average range or above, although the range of scores varies widely across studies. In addition, other studies in which the Denver Developmental Screening Test or the Bayley Scales of Infant Development were used reported normal development in children with mild NE, none of whom had CP.
Two to six years, without CP
In all of the selected studies [8, 9, 43, 44, 53], children with mild NE showed average general intellectual abilities that equaled those of a healthy comparison group (Table 2). Although children with moderate NE also performed in the average range, their abilities were significantly below those of children with mild NE and a reference group. The general intellectual abilities of children with severe NE were assessed in only one study. These children showed very low levels of functioning: a mean IQ of 37.1 was found [43]. In two studies, no grades of NE were recorded [9, 53], which hampers interpretation of these data. Rates of average or above-average functioning were 85% and 64%, respectively.
Seven to nine years, without CP
At school age, children with mild NE showed age-appropriate levels of general intellectual ability and performed as well as healthy comprison children on standardized testing [32, 46, 47] (Table 3). Although most children with moderate NE obtained significantly lower scores on intelligence measures than children with mild NE and comparison groups, their ability levels were in the average range.
Educational achievement
Relative strengths and weaknesses in cognitive functioning can become visible when children have to acquire specific scholastic abilities in school. A test of school readiness showed that 5.5-year-old children with mild and moderate NE were not significantly behind compared to comparison children [24]. However, children with moderate NE achieved lower scores in all domains, and 42% of them scored more than 1 SD below the average of the standardization group. Children with severe NE showed low school readiness scores at the age of 5.5 years. Table 4 gives an overview of the results of four studies, which assessed reading, writing, spelling and mathematics/arithmetic in school-aged children [32, 35, 46, 47]. Survivors of mild NE compared to a comparison group were not significantly more often more than one grade behind in reading, spelling and arithmetic. In these domains, all children with severe NE were more than one grade behind the expected level for their age. The group with moderate NE appeared to have distinct difficulties in reading, spelling and arithmetic/mathematics. Writing has been found to be less developed in only one subgroup of one of the studies.
Neuropsychological functions
Studies of specific neuropsychological functions yielded results that are similar to the findings on general intellectual ability and educational achievement (Tables 5, 6, 7 and 8). Children who suffered from mild NE tended to show normal receptive vocabulary and visual-motor integration at 3.5, 5.5 and 8 years of age [43, 44, 46]. As is the case in other cognitive domains, the severe group was impaired, and their ability levels were below those of children with mild or moderate NE and comparison groups. The moderate group showed an ambiguous pattern of performance. Deficits were found in receptive vocabulary, language and visual-motor integration. In contrast, attention and executive functions, visuospatial ability, and memory and learning were normal in a subgroup of 7-year-old children [29]. Although memory and learning did not seem to be impaired in this subgroup, these children had difficulties in the sub-domains narrative memory and sentence repetition, compared to intact memory for faces, names, everyday events and intact orientation. In addition, auditory verbal learning and delayed recall was impaired in a group of 9- and a group of 16-year-old children with moderate NE [31, 47]. Tests of other auditory skills in the former showed intact auditory attention and concentration, language and binaural separation, in contrast to impaired binaural integration, and sequencing and labeling [47].
Behavioral problems
So far, few researchers have addressed the issue of behavioral problems in children with a history of NE. Four studies were identified that met the selection criteria. In general, hyperactivity was more often present in children with moderate NE, but not in children with mild NE [32, 35, 44]. In addition, one study that used parent’s observations of their child’s behavior found more problems related to tractability, aggression, passivity and anxiety in a mixed group of children with NE compared to a control group [35]. No differences were found, however, related to attention, habituation and impulsivity. A recent study reported an unexpectedly high proportion of children with moderate and severe NE that developed an autism spectrum disorder [5]. However, the inclusion criteria differed considerably from those of Sarnat. Both groups can be classified as moderate NE when Sarnat’s criteria are applied. In addition, the authors explicitly did not exclude etiologies of NE other than PA.
Discussion and conclusion
In the introduction section of this review, problems were mentioned with regard to the comparability of different outcome studies of NE. Even after careful selection on the basis of predetermined criteria, comparing studies remained difficult. The main reason for this was the way test results were presented. In many cases, outcome of the children was reported in two or more categories, e.g., normal/mildly delayed/abnormal. One of the problems that arise due to such categorization, is the subjectivity involved in determining the cut-off points of the categories. Often, these cut-off points seemed to be chosen arbitrarily or not justified at all. Another issue that adds to this problem is the use of different tests of development, cognitive ability or behavioral problems. One can hardly compare, for example, the proportions of children with developmental delay in two studies when these are represented by a score below 85 on the Bayley Scales of Infant Development in one of the studies and by a score below 70 on the Griffiths Mental Developmental Scales in the other. To enable readers to compare studies, results of significance testing between subgroups of children with NE and comparison groups were added to the tables whenever available.
Children with a history of NE who suffer from CP at the age of 12 months are often also severely developmentally delayed. Estimates vary between 77% [12] and 90% [24]. Of the children without CP, those with mild NE have an excellent prognosis. Their intelligence, educational achievement and neuropsychological functions are comparable to healthy peers at least until middle childhood.
Children with severe NE appear to be impaired in every cognitive domain. They are less intelligent and perform worse at school and at neuropsychological tests than both healthy controls and children with mild or moderate NE.
As was expected, the children with moderate NE seem to form the most heterogeneous group. In general, these children are significantly less intelligent than children with mild NE and healthy peers, but their scores are still in the average range. And although these children are equally ready to go to school as other children at the age of 5.5 years, when measured in middle childhood, they perform less well in the domains reading, spelling and mathematics. On the basis of these results, it is possible that children with moderate NE fall behind in school, but do not show clear deficits at standardized testing. Another issue is raised by the relatively broad range of performance of children with moderate NE. Calculating mean IQ scores masks individual differences, which are large in groups of children with moderate NE. Therefore, means do not describe this group well, and it would be more informative to look at the distribution of scores. The description of neuropsychological functions in this review is based on six studies, in which five different tests were used in five different samples of children with moderate NE. These studies do not provide sufficient and comparable data to draw reliable conclusions about patterns of neuropsychological strengths and difficulties. The heterogeneous nature of the group of children with moderate NE raises the question whether these children really form one group. Marlow et al. [32] divided a group of children with moderate NE according to the severity of the symptoms of the NE. The group with the least severe neonatal symptoms showed a few discrete neuropsychological difficulties, but was more intelligent and performed better in school than the group with the most severe neonatal symptoms. Marlow suggested a dose-response effect of hypoxia within this group with moderate NE. This idea of a continuum of casualty with regard to morbidity has already been proposed by Low [29]. He suggested a theory of a critical threshold of asphyxia beyond which brain damage occurs. Beyond this threshold, a spectrum of minor and major deficits would occur. So, although the classification system of Sarnat [50] appears to be very useful for the prediction of the outcome of children with mild or severe NE, in case of moderate NE this qualitative system lacks predictive power. Future research should therefore focus on developing a reliable, more gradual model of prediction.
Another issue regarding prediction of outcome after NE also mainly concerns moderately affected children. It is still unclear whether NE has a general effect on intellectual abilities or leads to specific patterns of cognitive strengths and difficulties. This review shows that systematic, detailed research of neuropsychological function is still scarce. A few case reports, which are not included in this review, have shown specific impairments in episodic memory (memory for events), with relatively intact semantic memory (memory for facts) [21, 58]. However, these findings, which were based on small numbers of children, do not justify generalization to all children with NE and should therefore be replicated in larger, well-defined samples.
The introduction section of this review referred to possible associations between brain areas that are vulnerable to perinatal hypoxia and psychopathology, such as ADHD and autism. Long-term behavioral outcome of survivors of NE has received very little attention compared to neurological outcome and general intellectual ability. Only a few studies looked at the behavioral consequences of NE. Those studies found elevated rates of hyperactivity and autism in children with moderate NE. Evidence of hyperactivity and impulsive behavior has also been found in animal studies of PA [1, 60]. These findings suggest that, in addition to cognitive impairment, children with NE could be at risk of developing behavioral problems. Because of the clinical implications this would have, future research should include behavioral monitoring of all children with NE.
Long-term follow-up of children with NE has been advocated before, including the optimal ages of assessment of specific functions [48]. Recommendations include assessment of general cognitive and adaptive behavioral functioning at preschool age, and, in some cases, school achievement tests and a neuropsychological screening at school age. The authors of this review would like to go one step further in recommending standard screening for behavioral problems in all children with NE, regardless of their level of cognitive functioning. In addition, detailed neuropsychological assessment is recommended especially in all children with moderate NE.
From this review it is concluded that general intellectual, educational and neuropsychological outcomes are consistently positive for children with mild NE and negative for severely affected children. However, children with moderate NE form a more heterogeneous group with respect to outcome. On average, intelligence scores are below those of children with mild NE and age-matched peers, but within the normal range. Difficulties have been found in the domains reading, spelling and arithmetic/mathematics. So far, neuropsychological functioning of children with NE has received relatively little attention. The studies that were selected for this review have yielded ambiguous results in children with moderate NE. A few studies suggest elevated rates of hyperactivity in children with moderate NE and autism in children with moderate and severe NE. Therefore, behavioral monitoring is required for all children with NE. In addition, systematic, detailed neuropsychological examination is needed especially for children with moderate NE.

neuropsychology
asphyxia neonatorum
behavioral problems
hypoxia, brain
hypoxia-ischemia, brain