A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children.

OBJECTIVE: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment in children may reduce therapeutic effectiveness and thus affect their later life. METHODS: To address the need for suitable assessment measures, this paper proposes a spatiotemporal deep learning framework for scalp electroencephalogram (EEG)-based automated pain assessment in children. The dataset comprises scalp EEG data recorded from 33 pediatric patients with an arterial puncture as a pain stimulus. Two electrode reduction plans in line with clinical findings are proposed. Combining three-dimensional hand-crafted features and preprocessed raw signals, the proposed transformer-based pain assessment network (STPA-Net) integrates both spatial and temporal information. RESULTS: STPA-Net achieves superior performance with a subject-independent accuracy of 87.83% for pain recognition, and outperforms other state-of-the-art approaches. The effectiveness of electrode combinations is explored to analyze pain-related cortical activities and correspondingly reduce cost. The two proposed electrode reduction plans both demonstrate competitive pain assessment performance qualitatively and quantitatively. CONCLUSION AND SIGNIFICANCE: This study is the first to develop a scalp EEG-based automated pain assessment for children adopting a method that is objective, standardized, and consistent. The findings provide a potential reference for future clinical research.

Cost-efficient and Custom Electrode-holder Assembly Infrastructure for EEG Recordings.

Mobile electroencephalogram (EEG)-sensing technologies have rapidly progressed and made the access of neuroelectrical brain activity outside the laboratory in everyday life more realistic. However, most existing EEG headsets exhibit a fixed design, whereby its immobile montage in terms of electrode density and coverage inevitably poses a great challenge with applicability and generalizability to the fundamental study and application of the brain-computer interface (BCI). In this study, a cost-efficient, custom EEG-electrode holder infrastructure was designed through the assembly of primary components, including the sensor-positioning ring, inter-ring bridge, and bridge shield. It allows a user to (re)assemble a compact holder grid to accommodate a desired number of electrodes only to the regions of interest of the brain and iteratively adapt it to a given head size for optimal electrode-scalp contact and signal quality. This study empirically demonstrated its easy-to-fabricate nature by a low-end fused deposition modeling (FDM) 3D printer and proved its practicability of capturing event-related potential (ERP) and steady-state visual-evoked potential (SSVEP) signatures over 15 subjects. This paper highlights the possibilities for a cost-efficient electrode-holder assembly infrastructure with replaceable montage, flexibly retrofitted in an unlimited fashion, for an individual for distinctive fundamental EEG studies and BCI applications.

Validation of brain-derived signals in near-infrared spectroscopy through multivoxel analysis of concurrent functional magnetic resonance imaging.

Near-infrared spectroscopy (NIRS) is a convenient and safe brain-mapping tool. However, its inevitable confounding with hemodynamic responses outside the brain, especially in the frontotemporal head, has questioned its validity. Some researchers attempted to validate NIRS signals through concurrent measurements with functional magnetic resonance imaging (fMRI), but, counterintuitively, NIRS signals rarely correlate with local fMRI signals in NIRS channels, although both mapping techniques should measure the same hemoglobin concentration. Here, we tested a novel hypothesis that different voxels within the scalp and the brain tissues might have substantially different hemoglobin absorption rates of near-infrared light, which might differentially contribute to NIRS signals across channels. Therefore, we newly applied a multivariate approach, a partial least squares regression, to explain NIRS signals with multivoxel information from fMRI within the brain and soft tissues in the head. We concurrently obtained fMRI and NIRS signals in 9 healthy human subjects engaging in an n-back task. The multivariate fMRI model was quite successfully able to predict the NIRS signals by cross-validation (interclass correlation coefficient = ∼0.85). This result confirmed that fMRI and NIRS surely measure the same hemoglobin concentration. Additional application of Monte-Carlo permutation tests confirmed that the model surely reflects temporal and spatial hemodynamic information, not random noise. After this thorough validation, we calculated the ratios of the contributions of the brain and soft-tissue hemodynamics to the NIRS signals, and found that the contribution ratios were quite different across different NIRS channels in reality, presumably because of the structural complexity of the frontotemporal regions. Hum Brain Mapp 38:5274-5291, 2017. © 2017 Wiley Periodicals, Inc.

Association between sex inequality in animal protein intake and economic development in the Papua New Guinea highlands: The carbon and nitrogen isotopic composition of scalp hair and fingernail.

OBJECTIVES: People in the Papua New Guinea Highlands consume sweet potatoes as their dietary staple; consumption of animal protein is limited. In such societies with marginal protein intake, the intra-household allocation of animal protein in terms of sex or age is of importance. The objective of this study was to investigate how the allocation pattern of protein-rich foods by sex and age is associated with economic development in the Papua New Guinea Highlands. METHODS: The carbon and nitrogen isotopic compositions of fingernails, collected in 1995 in two areas (Tari and Port Moresby [the national capital where Tari migrants resided]), and of scalp hair, collected in 2007, 2012, and 2013 in three areas of different degree of economic development (Levani, Tari, and Goroka) were analyzed. RESULTS: Analysis of fingernail samples showed that δ(15)N was lower in rural communities than in the urban migrant community, while a sex difference in δ(15)N (higher in males than in females) was found in the former but not in the latter community. Age was not associated with either δ(15)N or δ(13)C values. The analysis of scalp hair samples showed that δ(15)N values were lowest in Levani, the least developed area. Furthermore, there were statistically significant sex differences in δ(15)N values in Levani but not in Tari and Goroka. Age was not associated with either δ(15)N or δ(13)C values. DISCUSSION: The sex inequality in animal protein consumption seems to have decreased as the communities in the Papua New Guinea Highlands have experienced economic development.

Quantification of physiological disparities and task performance in stress and control conditions.

In mental stress studies, cerebral activation and autonomic nervous system are important distinctly. This study aims to analyze disparities associated with scalp potential, which may have impact on autonomic activation of heart during mental stress. Ten healthy subjects participated in this study that performed arithmetic tasks in stress and control environment. Task difficulty was calculated from their correct responses. During the experiment, electroencephalogram (EEG) and electrocardiogram (ECG) signals were recorded concurrently. Sympathetic innervation of heart was estimated from heart rate (HR), which is extracted from the ECG. The value of theta Fz/alpha Pz was measured from EEG scalp potential. The results show a significant surge in the value of theta Fz/alpha Pz in stress as compared to baseline (p<0.013) and control (p<0.042). The results also present tachycardia while in stress as compared to baseline (p<0.05). Task difficulty in stress is also considerably higher than control environment (p<0.003).

Value of scalp delayed rhythmic ictal transformation (DRIT) in presurgical assessment of temporal lobe epilepsy.

OBJECTIVES: To estimate the localising and lateralising value of delayed rhythmic ictal transformation (DRIT) on the scalp EEG during presurgical assessment for temporal lobe epilepsy. METHODS: Two hundred and eighty eight seizures recorded simultaneously with scalp and foramen ovale (FO) electrodes were studied retrospectively in 110 patients. DRIT was defined as sustained regular rhythmic waveforms seen on scalp recordings after scalp seizure onset. The incidence and laterality of scalp DRIT was evaluated by comparison to FO electrodes. RESULTS: Scalp DRIT was seen in 192 seizures, 65% of which showed focal mesial temporal onset (FMTO) recorded by FO electrodes. FMTO onset was seen in 73% of the 122 seizures showing unilateral scalp DRIT, in 32% of the 62 seizures showing bilateral asymmetrical scalp DRIT, and in 14% of the seven seizures showing bilateral symmetrical DRIT on the scalp. Among the 89 seizures showing unilateral scalp DRIT and FMTO, both were ipsilateral in 70 seizures (79%). Among the 38 seizures showing bilateral asymmetrical DRIT and FMTO, the largest amplitude of scalp DRIT was ipsilateral to the FO onset in 27 seizures (71%). CONCLUSION: Two thirds of seizures showed unilateral scalp DRIT, which had a lateralising value of up to 79%. SIGNIFICANCE: Scalp DRIT is a reliable marker to lateralise seizure focus in patients without a focal seizure onset on the scalp EEG.

Scalp EEG acquisition in a low-noise environment: a quantitative assessment.

This pilot study investigates effects of an ultra shielded capsule at the low-noise underground laboratory (LSBB), Rustrel, France, when used to acquire scalp electroencephalogram (EEG). Analysis of EEG recordings from three volunteers confirms that clean EEG signals can be acquired in the LSBB capsule without the need for notch filtering. In addition, using different setups for acquiring EEG in the capsule, statistical analysis of power spectral densities based on a geodesic distance measure reveals that the laptop computer and patient module do not introduce any noise on recorded signals. Moreover, the current study shows that the backward counting task as a mental activity can be better detected using the EEG acquired in the capsule due to the higher level of â-band activities. The counting-relaxed â-band energy ratio is calculated using the S transform and compared between the hospital and capsule, revealing significantly higher values in the capsule (p < 0.05). Exploring the relative â-band energy (ratio of â-band energy to that of 0-12 Hz in counting state) reveals that the average of this measure is higher in the capsule for all subjects. Those results demonstrate the potential of the LSBB capsule for novel EEG studies, including establishing novel low-noise EEG benchmarks.

Assessment of a scalp EEG-based automated seizure detection system.

OBJECTIVE: The purpose of this study was to evaluate and validate an offline, automated scalp EEG-based seizure detection system and to compare its performance to commercially available seizure detection software. METHODS: The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ∼3653h with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (∼1200h with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persyst's Reveal®, version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms. RESULTS: The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24h, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24h, respectively. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p<0.05) smaller FDR. CONCLUSIONS: The study validates the performance of the IdentEvent™ seizure detection system. SIGNIFICANCE: With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.

The effects of electrode impedance on data quality and statistical significance in ERP recordings.

To determine whether data quality is meaningfully reduced by high electrode impedance, EEG was recorded simultaneously from low- and high-impedance electrode sites during an oddball task. Low-frequency noise was found to be increased at high-impedance sites relative to low-impedance sites, especially when the recording environment was warm and humid. The increased noise at the high-impedance sites caused an increase in the number of trials needed to obtain statistical significance in analyses of P3 amplitude, but this could be partially mitigated by high-pass filtering and artifact rejection. High electrode impedance did not reduce statistical power for the N1 wave unless the recording environment was warm and humid. Thus, high electrode impedance may increase noise and decrease statistical power under some conditions, but these effects can be reduced by using a cool and dry recording environment and appropriate signal processing methods.

Pharmacologic interventions in aging hair.

The appearance of hair plays an important role in people's overall physical appearance and self-perception. With today's increasing life-expectations, the desire to look youthful plays a bigger role than ever. The hair care industry has become aware of this and is delivering active products directed towards meeting this consumer demand. The discovery of pharmacological targets and the development of safe and effective drugs also indicate strategies of the drug industry for maintenance of healthy and beautiful hair. Hair aging comprises weathering of the hair shaft, decrease of melanocyte function, and decrease in hair production. The scalp is subject to intrinsic and extrinsic aging. Intrinsic factors are related to individual genetic and epigenetic mechanisms with interindividual variation: prototypes are familial premature graying, and androgenetic alopecia. Currently available pharmacologic treatment modalities with proven efficacy for treatment of androgenetic alopecia are topical minoxidil and oral finasteride. Extrinsic factors include ultraviolet radiation and air pollution. Experimental evidence supports the hypothesis that oxidative stress also plays a role in hair aging. Topical anti-aging compounds include photoprotectors and antioxidants. In the absence of another way to reverse hair graying, hair colorants remain the mainstay of recovering lost hair color. Topical liposome targeting for melanins, genes, and proteins selectively to hair follicles are currently under investigation.

An efficient algorithm for computing multishell spherical volume conductor models in EEG dipole source localization.

Computationally localizing electrical current sources of the electroencephalographic signal requires a volume conductor model which relates theoretical scalp potentials to the dipolar source located within the modeled brain. The commonly used multishell spherical model provides this source-potential relationship using a sum of infinite series whose computation is difficult. This paper provides a closed-form approximation to this sum based on an optimal fitting to the weights of the Legendre polynomials. The second-order (third-order) approximation algorithm, implemented by a provided C-routine, requires only 100 (140) floating point operations to compute a single scalp potential in response to an arbitrary current dipole located within a four-shell spherical volume conductor model. This cost of computation represents only 6.3% (8.9%) of that required by the direct method. The relative mean square error, measured by using 20,000 random dipoles distributed within the modeled brain, in only 0.29% (0.066%).