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Register a new userPain Analysis, in Premature Infants, Using Near Infrared Spectroscopy (NIRS)
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Background: The role of neonatal pain on the developing nervous system is not completely understood, but evidence suggests that sensory pathways are influenced by an infants pain experience. Research has shown that an infants previous pain experiences lead to an increased, and likely abnormal, response to subsequent painful stimuli. We are working to improve neonatal pain detection through automated devices that continuously monitor an infant. The current study outlines some of the initial steps we have taken to evaluate Near Infrared Spectroscopy (NIRS) as a technology to detect neonatal pain. Our findings may provide neonatal intensive care unit (NICU) practitioners with the data necessary to monitor and perhaps better manage an abnormal pain response. Methods: A prospective pilot study was conducted to evaluate nociceptive evoked cortical activity in preterm infants. NIRS data were recorded for approximately 10 minutes prior to an acute painful procedure and for approximately 10 minutes after the procedure. Individual data collection events were performed at a weekly maximum frequency. Eligible infants included those admitted to the Tampa General Hospital (TGH) NICU with a birth gestational age of less than 37 weeks. Results: A total of 15 infants were enrolled and 25 individual studies were completed. Analysis demonstrated a statistically significant difference between the median of the pre- and post-painful procedure data sets in each infants first NIRS collection (p value = 0.01). Conclusions: Initial analysis shows NIRS may be useful in detecting acute pain. An acute painful procedure is typically followed by a negative deflection in NIRS readings.
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The technique of near infrared spectroscopy (NIRS) allows to measure the oxygenation of the brain tissue. The particular problems involved in detecting regional brain oxygenation (rSO2) are discussed. The dominant chromophore (light absorber) in tissue is water. Only in the NIR light region of 650-1000 nm, the overall absorption is sufficiently low, and the NIR light can be detected across a thick layer of tissues, among them the skin, the scull and the brain. In this region, there are many absorbing light chromophores, but only three are important as far as the oxygenation is concerned. They are the hemoglobin (HbO2), the deoxy-hemoglobin (Hb) and cytochrome oxidase (CtOx). The devices that were used in our experiments were : Somanetics INVOS Brain Oximeter (IBO) and Toomims HEG spectrophotometer. The performances of both devices were compared including their merits and drawbacks. The IBO use for research has two drawbacks: the sampling rate is too small and the readings are limited to only two significant digits. The HEG device does not have these drawbacks, but is not developed sufficiently at this time to measure rSO2. We have measured the HEG readings and compared them with the rSO2 readings of the IBO. Results of an experiment are presented whose aim is to explore the relationship between respiration and cerebral oxygenation. Measurements of end tidal CO2 (EtCO2) were taken simultaneously with cerebral oxygen saturation (rSO2) using the INVOS Cerebral Oximeter of Somanetics. Due to the device limitations we could explore only subjects who could perform with a breathing rate of around 2/min or less. The results of all subjects clearly show a periodic change of cerebral oxygenation with the same period as the breathing exercises.
Bedside caregivers assess infants pain at constant intervals by observing specific behavioral and physiological signs of pain. This standard has two main limitations. The first limitation is the intermittent assessment of pain, which might lead to missing pain when the infants are left unattended. Second, it is inconsistent since it depends on the observers subjective judgment and differs between observers. The intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term consequences. To mitigate these limitations, the current standard can be augmented by an automated system that monitors infants continuously and provides quantitative and consistent assessment of pain. Several automated methods have been introduced to assess infants pain automatically based on analysis of behavioral or physiological pain indicators. This paper comprehensively reviews the automated approaches (i.e., approaches to feature extraction) for analyzing infants pain and the current efforts in automatic pain recognition. In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.
A brain wide association study (BWAS) based on the logistic regression was first developed and applied to a large population of epilepsy patients (168) and healthy controls (136). It was found that the most significant links associated with epilepsy are those bilateral links with regions mainly belonging to the default mode network and subcortex, such as amygdala, fusiform gyrus, inferior temporal gyrus, hippocampus, temporal pole, parahippocampal gyrus, insula, middle occipital gyrus, cuneus. These links were found to have much higher odd ratios than other links, and all of them showed reduced functional couplings in patients compared with controls. Interestingly, with the increasing of the seizure onset frequency or duration of illness, the functional connection between these bilateral regions became further reduced. On the other hand, as a functional compensation and brain plasticity, connections of these bilateral regions to other brain regions were abnormally enhanced and became even much stronger with the increase of the seizure onset frequency. Furthermore, patients had higher network efficiencies than healthy controls, and the seizure onset frequency was found to be positively correlated with the network efficiency. A negative correlation between the bilateral connection and the network efficiency was also observed. To further validate our findings, we then employed our BWAS results in discriminating patients from healthy controls and the leave-one-out accuracy was around 78%. Given the fact that a genome-wide association study with a large cohort has failed to identify any significant association between genes and epilepsy, our study could provide us with a set of endophenotypes for further study.
The loss of melanized neurons in the substantia nigra pars compacta is a primary feature in Parkinsons disease (PD). Iron deposition occurs in conjunction with this loss. Loss of nigral neurons should remove barriers for diffusion and increase diffusivity of water molecules in regions undergoing this loss. In metrics from single-compartment diffusion tensor imaging models, these changes should manifest as increases in mean diffusivity and the free water compartment as well as and reductions in fractional anisotropy. However, studies examining nigral diffusivity changes from PD with single-compartment models have yielded inconclusive results and emerging evidence in control subjects indicates that iron corrupts diffusivity metrics derived from single-compartment models. Iron-sensitive data and diffusion data were analyzed in two cohorts. The effect of iron on diffusion measures from single- and bi-compartment models was assessed in both cohorts. Measures sensitive to the free water compartment and iron content were found to increase in substantia nigra of the PD group in both cohorts. However, diffusion markers derived from the single-compartment model were not replicated across cohorts. Correlations were seen between single-compartment diffusion measures and iron markers in the discovery cohort and validation cohort but no correlation was observed between a measure from the bi-compartment model related to the free water compartment and iron markers in either cohort. The variability of single-compartment nigral diffusion metrics in PD may be attributed to competing influences of increased iron content, which drives diffusivity down, and increases in the free water compartment, which drives diffusivity up. In contrast to diffusion metrics derived from the single-compartment model, no relationship was seen between iron and the free water compartment in substantia nigra.
A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI) and infer properties about the white matter microstructure. However, a head-to-head comparison of DW-MRI models is critically missing in the field. To address this deficiency, we organized the White Matter Modeling Challenge during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed at identifying the DW-MRI models that best predict unseen DW data. in vivo DW-MRI data was acquired on the Connectom scanner at the A.A.Martinos Center (Massachusetts General Hospital) using gradients strength of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the whole dataset, and their models were ranked on their ability to predict the remaining unseen quarter of data. In this paper we provide both an overview and a more in-depth description of each evaluated model, report the challenge results, and infer trends about the model characteristics that were associated with high model ranking. This work provides a much needed benchmark for DW-MRI models. The acquired data and model details for signal prediction evaluation are provided online to encourage a larger scale assessment of diffusion models in the future.
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