Associations between neurological examination at term-equivalent age and cerebral hemodynamics and oxygen metabolism in infants born preterm.

Background: Infants born at 29-36 weeks gestational age (GA) are at risk of experiencing neurodevelopmental challenges. We hypothesize that cerebral hemodynamics and oxygen metabolism measured by bedside optical brain monitoring are potential biomarkers of brain development and are associated with neurological examination at term-equivalent age (TEA). Methods: Preterm infants (N = 133) born 29-36 weeks GA and admitted in the neonatal intensive care unit were enrolled in this prospective cohort study. Combined frequency-domain near infrared spectroscopy (FDNIRS) and diffuse correlation spectroscopy (DCS) were used from birth to TEA to measure cerebral hemoglobin oxygen saturation and an index of microvascular cerebral blood flow (CBF i ) along with peripheral arterial oxygen saturation (SpO2). In combination with hemoglobin concentration in the blood, these parameters were used to derive cerebral oxygen extraction fraction (OEF) and an index of cerebral oxygen metabolism (CMRO2i ). The Amiel-Tison and Gosselin Neurological Assessment was performed at TEA. Linear regression models were used to assess the associations between changes in FDNIRS-DCS parameters from birth to TEA and GA at birth. Logistic regression models were used to assess the associations between changes in FDNIRS-DCS parameters from birth to TEA and neurological examination at TEA. Results: Steeper increases in CBF i (p < 0.0001) and CMRO2i (p = 0.0003) were associated with higher GA at birth. Changes in OEF, CBF i , and CMRO2i from birth to TEA were not associated with neurological examination at TEA. Conclusion: In this population, cerebral FDNIRS-DCS parameters were not associated with neurological examination at TEA. Larger increases in CBF i and CMRO2i from birth to TEA were associated with higher GA. Non-invasive bedside FDNIRS-DCS monitoring provides cerebral hemodynamic and metabolic parameters that may complement neurological examination to assess brain development in preterm infants.

Opioid analgesia and temperature regulation are associated with EEG background activity and MRI outcomes in neonates with mild-to-moderate hypoxic-ischemic encephalopathy undergoing therapeutic hypothermia.

BACKGROUND: Therapeutic hypothermia (TH) without sedation may lead to discomfort, which may be associated with adverse consequences in neonates with hypoxic-ischemic encephalopathy (HIE). The aim of this study was to assess the association between level of exposure to opioids and temperature, with electroencephalography (EEG) background activity post-TH and magnetic resonance imaging (MRI) brain injury in neonates with HIE. METHODS: Thirty-one neonates with mild-to-moderate HIE who underwent TH were identified. MRIs were reviewed for presence of brain injury. Quantitative EEG background features including EEG discontinuity index and spectral power densities were calculated during rewarming and post-rewarming periods. Dose of opioids administered during TH and temperatures were collected from the medical charts. Multivariable linear and logistic regression analyses were conducted to assess the associations between cumulative dose of opioids and temperature with EEG background and MRI while adjusting for markers of HIE severity. RESULTS: Higher opioid doses (ß = -0.21, p = 0.02) and reduced skin temperature (ß = 0.14, p < 0.01) were associated with lower EEG discontinuity index recorded post-TH. Higher opioid doses (ß = 0.75, p = 0.01) and reduced skin temperature (ß = -0.39, p = 0.02) were also associated with higher EEG Delta power post-TH. MRI brain injury was observed in 14 patients (45%). In adjusted regression analyses, higher opioid doses (OR = 0.00; 95%CI: 0-0.19; p = 0.01), reduced skin temperature (OR = 41.19; 95%CI: 2.27-747.86; p = 0.01) and reduced cooling device output temperature (OR = 1.91; 95%CI: 1.05-3.48; p = 0.04) showed an association with lower odds of brain injury. CONCLUSIONS: Higher level of exposure to opioids and reduced skin temperature during TH in mild-to-moderate HIE were associated with improved EEG background activity post-TH. Moreover, higher exposure to opioids, reduced skin temperature and reduced device output temperature were associated with lower odds of brain injury on MRI.

Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean.

Functional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. Diffuse optical tomography (DOT) consists of reconstructing the optical density changes measured from scalp channels to the oxy-/deoxy-hemoglobin concentration changes within the cortical regions. In the present study, we adapted a nonlinear source localization method developed and validated in the context of Electro- and Magneto-Encephalography (EEG/MEG): the Maximum Entropy on the Mean (MEM), to solve the inverse problem of DOT reconstruction. We first introduced depth weighting strategy within the MEM framework for DOT reconstruction to avoid biasing the reconstruction results of DOT towards superficial regions. We also proposed a new initialization of the MEM model improving the temporal accuracy of the original MEM framework. To evaluate MEM performance and compare with widely used depth weighted Minimum Norm Estimate (MNE) inverse solution, we applied a realistic simulation scheme which contained 4000 simulations generated by 250 different seeds at different locations and 4 spatial extents ranging from 3 to 40[Formula: see text] along the cortical surface. Our results showed that overall MEM provided more accurate DOT reconstructions than MNE. Moreover, we found that MEM was remained particularly robust in low signal-to-noise ratio (SNR) conditions. The proposed method was further illustrated by comparing to functional Magnetic Resonance Imaging (fMRI) activation maps, on real data involving finger tapping tasks with two different montages. The results showed that MEM provided more accurate HbO and HbR reconstructions in spatial agreement with the main fMRI cluster, when compared to MNE.

Clinical yield of magnetoencephalography distributed source imaging in epilepsy: A comparison with equivalent current dipole method.

OBJECTIVE: Source localization of interictal epileptic discharges (IEDs) is clinically useful in the presurgical workup of epilepsy patients. It is usually obtained by equivalent current dipole (ECD) which localizes a point source and is the only inverse solution approved by clinical guidelines. In contrast, magnetic source imaging using distributed methods (dMSI) provides maps of the location and the extent of the generators, but its yield has not been clinically validated. We systematically compared ECD versus dMSI performed using coherent Maximum Entropy on the Mean (cMEM), a method sensitive to the spatial extent of the generators. METHODS: 340 source localizations of IEDs derived from 49 focal epilepsy patients with foci well-defined through intracranial EEG, MRI lesions, and surgery were analyzed. The comparison was based on the assessment of the sublobar concordance with the focus and of the distance between the source and the focus. RESULTS: dMSI sublobar concordance was significantly higher than ECD (81% vs 69%, P < 0.001), especially for extratemporal lobe sources (dMSI = 84%; ECD = 67%, P < 0.001) and for seizure free patients (dMSI = 83%; ECD = 70%, P < 0.001). The median distance from the focus was 4.88 mm for ECD and 3.44 mm for dMSI (P < 0.001). ECD dipoles were often wrongly localized in deep brain regions. CONCLUSIONS: dMSI using cMEM exhibited better accuracy. dMSI also offered the advantage of recovering more realistic maps of the generator, which could be exploited for neuronavigation aimed at targeting invasive EEG and surgical resection. Therefore, dMSI may be preferred to ECD in clinical practice. Hum Brain Mapp 39:218-231, 2018. © 2017 Wiley Periodicals, Inc.

Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy.

Fusion of electroencephalography (EEG) and magnetoencephalography (MEG) data using maximum entropy on the mean method (MEM-fusion) takes advantage of the complementarities between EEG and MEG to improve localization accuracy. Simulation studies demonstrated MEM-fusion to be robust especially in noisy conditions such as single spike source localizations (SSSL). Our objective was to assess the reliability of SSSL using MEM-fusion on clinical data. We proposed to cluster SSSL results to find the most reliable and consistent source map from the reconstructed sources, the so-called consensus map. Thirty-four types of interictal epileptic discharges (IEDs) were analyzed from 26 patients with well-defined epileptogenic focus. SSSLs were performed on EEG, MEG, and fusion data and consensus maps were estimated using hierarchical clustering. Qualitative (spike-to-spike reproducibility rate, SSR) and quantitative (localization error and spatial dispersion) assessments were performed using the epileptogenic focus as clinical reference. Fusion SSSL provided significantly better results than EEG or MEG alone. Fusion found at least one cluster concordant with the clinical reference in all cases. This concordant cluster was always the one involving the highest number of spikes. Fusion yielded highest reproducibility (SSR EEG = 55%, MEG = 71%, fusion = 90%) and lowest localization error. Also, using only few channels from either modality (21EEG + 272MEG or 54EEG + 25MEG) was sufficient to reach accurate fusion. MEM-fusion with consensus map approach provides an objective way of finding the most reliable and concordant generators of IEDs. We, therefore, suggest the pertinence of SSSL using MEM-fusion as a valuable clinical tool for presurgical evaluation of epilepsy.

MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy.

The purpose of this study is to develop and quantitatively assess whether fusion of EEG and MEG (MEEG) data within the maximum entropy on the mean (MEM) framework increases the spatial accuracy of source localization, by yielding better recovery of the spatial extent and propagation pathway of the underlying generators of inter-ictal epileptic discharges (IEDs). The key element in this study is the integration of the complementary information from EEG and MEG data within the MEM framework. MEEG was compared with EEG and MEG when localizing single transient IEDs. The fusion approach was evaluated using realistic simulation models involving one or two spatially extended sources mimicking propagation patterns of IEDs. We also assessed the impact of the number of EEG electrodes required for an efficient EEG-MEG fusion. MEM was compared with minimum norm estimate, dynamic statistical parametric mapping, and standardized low-resolution electromagnetic tomography. The fusion approach was finally assessed on real epileptic data recorded from two patients showing IEDs simultaneously in EEG and MEG. Overall the localization of MEEG data using MEM provided better recovery of the source spatial extent, more sensitivity to the source depth and more accurate detection of the onset and propagation of IEDs than EEG or MEG alone. MEM was more accurate than the other methods. MEEG proved more robust than EEG and MEG for single IED localization in low signal-to-noise ratio conditions. We also showed that only few EEG electrodes are required to bring additional relevant information to MEG during MEM fusion.

MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches.

Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm(2) to 30 cm(2), whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.