Unraveling the blue shift in porphyrin fluorescence in glioma: The 620 nm peak and its potential significance in tumor biology.

In glioma surgery, the low-density infiltration zone of tumors is difficult to detect by any means. While, for instance, 5-aminolevulinic acid (5-ALA)-induced fluorescence is a well-established surgical procedure for maximizing resection of malignant gliomas, a cell density in tumor tissue of 20-30% is needed to observe visual fluorescence. Hyperspectral imaging is a powerful technique for the optical characterization of brain tissue, which accommodates the complex spectral properties of gliomas. Thereby, knowledge about the signal source is essential to generate specific separation (unmixing) procedures for the different spectral characteristics of analytes and estimate compound abundances. It was stated that protoporphyrin IX (PpIX) fluorescence consists mainly of emission peaks at 634 nm (PpIX634) and 620 nm (PpIX620). However, other members of the substance group of porphyrins fluoresce similarly to PpIX due to their common tetrapyrrole core structure. While the PpIX634 signal has reliably been assigned to PpIX, it has not yet been analyzed if PpIX620 might result from a different porphyrin rather than being a second photo state of PpIX. We thus reviewed more than 200,000 spectra from various tumors measured in almost 600 biopsies of 130 patients. Insufficient consideration of autofluorescence led to artificial inflation of the PpIX620 peak in the past. Recently, five basis spectra (PpIX634, PpIX620, flavin, lipofuscin, and NADH) were described and incorporated into the analysis algorithm, which allowed more accurate unmixing of spectral abundances. We used the improved algorithm to investigate the PpIX620 signal more precisely and investigated coproporphyrin III (CpIII) fluorescence phantoms for spectral unmixing. Our findings show that the PpIX634 peak was the primary source of the 5-ALA-induced fluorescence. CpIII had a similar spectral characteristic to PpIX620. The supplementation of 5-ALA may trigger the increased production of porphyrins other than PpIX within the heme biosynthesis pathway, including that of CpIII. It is essential to correctly separate autofluorescence from the main PpIX634 peak to analyze the fluorescence signal. This article highlights the need for a comprehensive understanding of the spectral complexity in gliomas and suggests less significance of the 620 nm fluorescence peak for PpIX analysis and visualization.

Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD.

This study presents a methodology developed for estimating effective connectivity in brain networks (BNs) using multichannel scalp EEG recordings. The methodology uses transfer entropy as an information transfer measure to detect pair-wise directed information transfer between EEG signals within δ, θ, α, ß and γ-bands. The developed methodology is then used to study the properties of directed BNs in children with attention-deficit hyperactivity disorder (ADHD) and compare them with that of the healthy controls using both statistical and receiver operating characteristic (ROC) analyses. The results indicate that directed information transfer between scalp EEG electrodes in the ADHD subjects differs significantly compared to the healthy ones. The results of the statistical and ROC analyses of frequency-specific graph measures demonstrate their highly discriminative ability between the two groups. Specifically, the graph measures extracted from the estimated directed BNs in the ß-band show the highest discrimination between the ADHD and control groups. These findings are in line with the fact that ß-band reflects active concentration, motor activity, and anxious mental states. The reported results show that the developed methodology has the capacity to be used for investigating patterns of directed BNs in neuropsychiatric disorders.

EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review.

OBJECTIVES: Hypoxic ischaemic encephalopathy is a significant cause of mortality and morbidity in the term infant. Electroencephalography (EEG) is a useful tool in the assessment of newborns with HIE. This systematic review of published literature identifies those background features of EEG in term neonates with HIE that best predict neurodevelopmental outcome. METHODS: A literature search was conducted using the PubMed, EMBASE and CINAHL databases from January 1960 to April 2014. Studies included in the review described recorded EEG background features, neurodevelopmental outcomes at a minimum age of 12 months and were published in English. Pooled sensitivities and specificities of EEG background features were calculated and meta-analyses were performed for each background feature. RESULTS: Of the 860 articles generated by the initial search strategy, 52 studies were identified as potentially relevant. Twenty-one studies were excluded as they did not distinguish between different abnormal background features, leaving 31 studies from which data were extracted for the meta-analysis. The most promising neonatal EEG features are: burst suppression (sensitivity 0.87 [95% CI (0.78-0.92)]; specificity 0.82 [95% CI (0.72-0.88)]), low voltage (sensitivity 0.92 [95% CI (0.72-0.97)]; specificity 0.99 [95% CI (0.88-1.0)]), and flat trace (sensitivity 0.78 [95% CI (0.58-0.91)]; specificity 0.99 [95% CI (0.88-1.0)]). CONCLUSION: Burst suppression, low voltage and flat trace in the EEG of term neonates with HIE most accurately predict long term neurodevelopmental outcome. SIGNIFICANCE: This structured review and meta-analysis provides quality evidence of the background EEG features that best predict neurodevelopmental outcome.

Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features.

Perinatal hypoxia is a cause of cerebral injury in foetuses and neonates. Detection of foetal hypoxia during labour based on the pattern recognition of heart rate signals suffers from high observer variability and low specificity. We describe a new automated hypoxia detection method using time-frequency analysis of heart rate variability (HRV) signals. This approach uses features extracted from the instantaneous frequency and instantaneous amplitude of HRV signal components as well as features based on matrix decomposition of the signals' time-frequency distributions using singular value decomposition and non-negative matrix factorization. The classification between hypoxia and non-hypoxia data is performed using a support vector machine classifier. The proposed method is tested on a dataset obtained from a newborn piglet model with a controlled hypoxic insult. The chosen HRV features show strong performance compared to conventional spectral features and other existing methods of hypoxia detection with a sensitivity 93.3 %, specificity 98.3 % and accuracy 95.8 %. The high predictive value of this approach to detecting hypoxia is a substantial step towards developing a more accurate and reliable hypoxia detection method for use in human foetal monitoring.

Measuring time-varying information flow in scalp EEG signals: orthogonalized partial directed coherence.

This study aimed to develop a time-frequency method for measuring directional interactions over time and frequency from scalp-recorded electroencephalographic (EEG) signals in a way that is less affected by volume conduction and amplitude scaling. We modified the time-varying generalized partial directed coherence (tv-gPDC) method, by orthogonalization of the strictly causal multivariate autoregressive model coefficients, to minimize the effect of mutual sources. The novel measure, generalized orthogonalized PDC (gOPDC), was tested first using two simulated models with feature dimensions relevant to EEG activities. We then used the method for assessing event-related directional information flow from flash-evoked responses in neonatal EEG. For testing statistical significance of the findings, we followed a thresholding procedure driven by baseline periods in the same EEG activity. The results suggest that the gOPDC method 1) is able to remove common components akin to volume conduction effect in the scalp EEG, 2) handles the potential challenge with different amplitude scaling within multichannel signals, and 3) can detect directed information flow within a subsecond time scale in nonstationary multichannel EEG datasets. This method holds promise for estimating directed interactions between scalp EEG channels that are commonly affected by the confounding impact of mutual cortical sources.