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Magnetic resonance spectroscopy assessment of brain injury after moderate hypothermia in neonatal encephalopathy: a prospective multicentre cohort study

Summary Background In neonatal encephalopathy, the clinical manifestations of injury can only be reliably assessed several years after an intervention, complicating early prognostication and rendering trials of promising neuroprotectants slow and expensive. We aimed to determine the accuracy of thalamic proton magnetic resonance (MR) spectroscopy (MRS) biomarkers as early predictors of the neurodevelopmental abnormalities observed years after neonatal encephalopathy. Methods We did a prospective multicentre cohort study across eight neonatal intensive care units in the UK and USA, recruiting term and near-term neonates who received therapeutic hypothermia for neonatal encephalopathy. We excluded infants with life-threatening congenital malformations, syndromic disorders, neurometabolic diseases, or any alternative diagnoses for encephalopathy that were apparent within 6 h of birth. We obtained T1-weighted, T2-weighted, and diffusion-weighted MRI and thalamic proton MRS 4–14 days after birth. Clinical neurodevelopmental tests were done 18–24 months later. The primary outcome was the association between MR biomarkers and an adverse neurodevelopmental outcome, defined as death or moderate or severe disability, measured using a multivariable prognostic model. We used receiver operating characteristic (ROC) curves to examine the prognostic accuracy of the individual biomarkers. This trial is registered with ClinicalTrials.gov, number NCT01309711. Findings Between Jan 29, 2013, and June 25, 2016, we recruited 223 infants who all underwent MRI and MRS at a median age of 7 days (IQR 5–10), with 190 (85%) followed up for neurological examination at a median age of 23 months (20–25). Of those followed up, 31 (16%) had moderate or severe disability, including one death. Multiple logistic regression analysis could not be done because thalamic N-acetylaspartate (NAA) concentration alone accurately predicted an adverse neurodevelopmental outcome (area under the curve [AUC] of 0·99 [95% CI 0·94–1·00]; sensitivity 100% [74–100]; specificity 97% [90–100]; n=82); the models would not converge when any additional variable was examined. The AUC (95% CI) of clinical examination at 6 h (n=190) and at discharge (n=167) were 0·72 (0·65–0·78) and 0·60 (0·53–0·68), respectively, and the AUC of abnormal amplitude integrated EEG at 6 h (n=169) was 0·73 (0·65–0·79). On conventional MRI (n=190), cortical injury had an AUC of 0·67 (0·60–0·73), basal ganglia or thalamic injury had an AUC of 0·81 (0·75–0·87), and abnormal signal in the posterior limb of internal capsule (PLIC) had an AUC of 0·82 (0·76–0·87). Fractional anisotropy of PLIC (n=65) had an AUC of 0·82 (0·76–0·87). MRS metabolite peak-area ratios (n=160) of NAA–creatine (<1·29) had an AUC of 0·79 (0·72–0·85), of NAA–choline had an AUC of 0·74 (0·66–0·80), and of lactate–NAA (>0·22) had an AUC of 0·94 (0·89–0·97). Interpretation Thalamic proton MRS measures acquired soon after birth in neonatal encephalopathy had the highest accuracy to predict neurdevelopment 2 years later. These methods could be applied to increase the power of neuroprotection trials while reducing their duration. Funding National Institute for Health Research UK.


Supplementary
Infant who has seizure will be Moderate or Severe NE depending on the neurologic exam. Seizure with normal or mild NE or moderate NE on neurologic exam will be "Moderate NE". Seizure with severe NE will be "Severe NE". The level of encephalopathy will be assigned based on which level of signs (moderate or severe) predominates among the 6 categories. If moderate and severe signs are equally distributed, the designation is then based on the highest level in Category #1: The level of consciousness. Representative in-vivo spectra:

Supplementary
Spectra collected according to the protocol, with the LCModel fit overlaid (in red). These spectra are representative of the mean linewidth across all analysed spectra. The relationship between the spectra at each step (via T 1 or T 2 relaxation effects) is also highlighted.

Quality assurance:
The inter-site variation in MRS measurements was quantified across all scanners using a spherical phantom containing a buffered solution of 10mM N-acetylaspartate (NAA) and 10mM lactate (Lac). In vivo quality assurance included the visual assessment of motion from imaging before and after MRS; visual assessment of voxel placement accuracy; assessment of protocol adherence; manual rejection of motion corrupted sub-spectra; automated spectral corrections for both frequency and phase; and the rejection of spectra with linewidths outside the normal distribution of the full dataset.
Specific LCModel control parameters were, for TE=288ms: And for TE=60ms: The methyl peaks of NAA, NAAG, choline (Cho), phosphocreatine (PCr), and creatine (Cr) were separated from other groups in the basis spectra to allow quantification of individual relaxation rates. NAA+NAAG methyl peaks at ~2·0ppm were combined and referred to as 'NAA', and PCr+Cr methyl peaks at ~3·0ppm were combined and referred to as 'Cr' due to strong covariance. Thr+Lac were combined and referred to as 'Lac' due to strong covariance. A single peak was used to fit the choline signal at ~3·2ppm and referred to as 'Cho'. Water unsuppressed signals were quantified using HLSVD. The parenchymal water signal was quantified by biexponential fit of the water unsuppressed series, with a long T 2 component (fixed at 500ms) accounting for mobile water.
[NAA] was calculated from the fitted NAA methyl singlets throughout the experiment, comparing the relaxation corrected signal to the relaxation corrected unsuppressed water signal, after partial volume correction. This process is outlined in the following figure: Calculation steps to derive thalamic NAA concentration, [NAA], from the data collected in the various steps of the protocol. Black boxes show the input data, either obtained from the LCModel/HLSVD fits or reference values. Red boxes and text denote calculated quantities from the NAA resonance, and blue boxes and text denote calculated quantities from the water signal. Equations accompany the different calculation steps.
, denotes the NAA signal intensity in the corresponding protocol step, with the relaxation corrected intensities for NAA and parenchyma water given by and respectively.

Diffusion MRI
Protocol: 32 direction 2D spin-echo echo-planar imaging, b=0,750 s/mm 2 , TE=49 ms, 1·75×1·75×2 mm 3 voxels, parallel imaging acceleration factor 2·0 Quality assurance and processing: Raw data was assessed for excessive motion and acquisition artefacts, before isolating the brain signal and undertaking corrections for eddy currents and motion using the Functional MRI of the Brain Software Library (FSL v5·0·9, with eddy openmp patch).
Diffusion tensors were then calculated in FSL, and the sum-of-squares error maps inspected for underlying artefacts. Datasets of sufficient quality were spatially normalised to a group-wise template in DTI-TK via rigid, affine and diffeomorphic transformations. Major white matter tracts were selected by thresholding according to fractional anisotropy (FA>0·2) and skeletonised using FSL.
To select a region of interest in the posterior limbs of the internal capsule, the mean FA skeleton was manually segmented to isolate these tracts, as shown below:

Mean FA skeleton thresholded to include only major white matter tracts (green). Manually segmented PLICs are shown overlaid (red)
The mean FA and standard deviation within this region of interest was then used for assessing prognostic accuracy.
Tract-based spatial statistics (FSL) was then used to examine relationships between FA and neurological outcomes across all of the major white matter tracts included in the mean skeleton. These analyses were corrected for multiple comparisons using threshold-free cluster enhancement.

Supplementary Table 3: Pattern of injury score on conventional magnetic resonance imaging
1. Posterior limb of the internal capsule (PLIC) score: 0=normal, 1=equivocal (reduced or asymmetrical signal intensity), 2=loss (reversed or abnormal signal intensity bilaterally on T 1 and/or T 2 weighted sequences).
2. Basal ganglia and thalamic (BGT) score: 0=normal, 1=mild (focal abnormal signal intensity), 2=moderate (multifocal abnormal signal intensity), 3=severe (widespread abnormal signal intensity) 3. White matter (WM) score: 0=normal, 1=mild (exaggerated long T 1 and long T 2 in periventricular white matter only), 2=moderate (long T 1 and long T 2 extending out to subcortical white matter and /or focal punctate lesions or focal area of infarction), 3=severe widespread abnormalities including overt infarction, haemorrhage, and long T 1 and long T 2 . Infarction was classified as areas of excessive long T 1 and long T 2 with either loss of grey-white matter differentiation (usually within first week exaggerated grey-white matter differentiation after the first week). Optic radiation was scored as abnormal if it demonstrated increased signal intensity on T 1 weighted images.
Each infant was then given a score for PLIC, BGT, WM, and cortex 13  Discharge neurological examination score, Lactate/N-acetyl aspartate, N-acetyl-aspartate concentration, Nacetyl-aspartate/Creatine, N-acetyl-aspartate/Choline, Basal ganglia/thalamic MRI score, white matter MRI score, cortical score MRI score, PLIC MRI score, and amplitude integrated electroencephalography score were included in the regression model.

Supplementary
The multivariable analyses for the Bayley scores adjusted for gestational age and postnatal age. However, these were not adjusted for the analyses involving an adverse outcome. Firstly, our analyses suggested that neither variable was significantly associated with an adverse outcome, and thus is unlikely to confound the relationship between the other variables and this outcome. Additionally, as mentioned in the paper, the strength of association between NAA and an adverse outcome was so strong that almost all variation in the outcome was explained, and it was not mathematically possible to include further predictor variables (such as gestational age) in the model.
For gestational age, the data suggested a mean (±SD) of 39.9 ± 1.5 for those with no adverse outcome, and 39.6 ± 1.4 with an adverse outcome. This was not significant (p=0.27 from unpaired t-test).
For post-natal age, the data suggested a median [IQR] of 7 [5, 10] days for those with no adverse outcome, and 8 [6,12] with an adverse outcome. This was not significant (p=0.66 from Mann-Whitney test).
*only the ROC curves of the most commonly used biomarkers in a clinical setting are displayed for the sake of clarity. The area under curve of all biomarkers examined is presented in figure 2 in the main text.