Large Intersubject Variability Research Articles

Myogenic artifacts masquerade as neuroplasticity in the auditory frequency-following response.

The frequency-following response (FFR) is an evoked potential that provides a neural index of complex sound encoding in the brain. FFRs have been widely used to characterize speech and music processing, experience-dependent neuroplasticity (e.g., learning and musicianship), and biomarkers for hearing and language-based disorders that distort receptive communication abilities. It is widely assumed that FFRs stem from a mixture of phase-locked neurogenic activity from the brainstem and cortical structures along the hearing neuraxis. In this study, we challenge this prevailing view by demonstrating that upwards of ~50% of the FFR can originate from an unexpected myogenic source: contamination from the postauricular muscle (PAM) vestigial startle reflex. We measured PAM, transient auditory brainstem responses (ABRs), and sustained frequency-following response (FFR) potentials reflecting myogenic (PAM) and neurogenic (ABR/FFR) responses in young, normal-hearing listeners with varying degrees of musical training. We first establish that PAM artifact is present in all ears, varies with electrode proximity to the muscle, and can be experimentally manipulated by directing listeners' eye gaze toward the ear of sound stimulation. We then show this muscular noise easily confounds auditory FFRs, spuriously amplifying responses 3-4-fold with tandem PAM contraction and even explaining putative FFR enhancements observed in highly skilled musicians. Our findings expose a new and unrecognized myogenic source to the FFR that drives its large inter-subject variability and cast doubt on whether changes in the response typically attributed to neuroplasticity/pathology are solely of brain origin.

Neural responses to oral administration of erythritol vs. sucrose and sucralose explain differences in subjective liking ratings

IntroductionHigh sugar intake is associated with many chronic diseases. However, non-caloric sweeteners (NCSs) might fail to successfully replace sucrose due to the mismatch between their rewarding sweet taste and lack of caloric content. The natural NCS erythritol has been proposed as a sugar substitute due to its satiating properties despite being non-caloric. We aimed to compare brain responses to erythritol vs. sucrose and the artificial NCS sucralose in a priori taste, homeostatic, and reward brain regions of interest (ROIs). MethodsWe performed a within-subject, single-blind, counterbalanced fMRI study in 30 healthy men (mean ± SEM age:24.3 ± 0.8 years, BMI:22.3 ± 0.3 kg/m2). Before scanning, we individually matched the concentrations of both NCSs to the perceived sweetness intensity of a 10% sucrose solution. During scanning, participants received 1 mL sips of the individually titrated equisweet solutions of sucrose, erythritol, and sucralose, as well as water. After each sip, they rated subjective sweetness liking. ResultsLiking ratings were significantly higher for sucrose and sucralose vs. erythritol (both pHolm = 0.0037); water ratings were neutral. General Linear Model (GLM) analyses of brain blood oxygen level-depended (BOLD) responses at qFDR<0.05 showed no differences between any of the sweeteners in a priori ROIs, but distinct differences were found between the individual sweeteners and water. These results were confirmed by Bayesian GLM and machine learning-based models. However, several brain response patterns mediating the differences in liking ratings between the sweeteners were found in whole-brain multivariate mediation analyses. Both subjective and neural responses showed large inter-subject variability. ConclusionWe found lower liking ratings in response to oral administration of erythritol vs. sucrose and sucralose, but no differences in neural responses between any of the sweeteners in a priori ROIs. However, differences in liking ratings between erythritol vs. sucrose or sucralose are mediated by multiple whole-brain response patterns.

A learnable continuous wavelet-based multi-branch attentive convolutional neural network for spatio–spectral–temporal EEG signal decoding

Deep learning methods, particularly convolutional neural networks (CNNs), have advanced the field of motor imagery (MI) by effectively extracting spatio-spectral–temporal (SST) features from electroencephalography (EEG) signals. Image-based CNNs using time–frequency analysis such as wavelet transform have outperformed signal-based CNNs in MI-EEG classification. However, the spatial characteristics of EEG signals can be neglected due to the high dimensionality of spectral–temporal feature space. Moreover, the performance hinges on whether the predefined transformation can capture the class-discriminative spectral–temporal representations of EEG signals well. Despite attempts to alleviate these challenges by selecting EEG electrodes or restricting spectral–temporal feature space, large inter-subject variabilities have impeded successful MI-EEG decoding. In this paper, we propose a learnable continuous wavelet-based multi-branch attentive CNN framework for decoding MI-EEG signals. Our method is designed to automatically generate class-discriminative SST representations of EEG signals within subject-specific sub-bands by utilizing learnable wavelet-based convolutions. Additionally, it employs multi-branch attentive CNNs for the effective and efficient extraction of local and global SST features. Our comprehensive evaluation on two public datasets demonstrates that the proposed method significantly outperformed state-of-the-art methods. We also conduct an ablation study under various configuration settings within the proposed framework to demonstrate the effectiveness of our method. The proposed method provides a powerful and innovative approach to MI-EEG signal decoding, with implications for personalized EEG decoding.

EEG-Based Subject-Independent Emotion Recognition Using Gated Recurrent Unit and Minimum Class Confusion

Automatic emotion recognition based on electroencephalogram (EEG) has attracted rapidly increasing interests. Due to large inter-subject variabilities, subject-independent emotion recognition faces great challenges. Recently, domain adaptation methods have been successfully applied in this field due to their ability to align features from different subjects. However, since EEG signals corresponding to some emotions have similar oscillation patterns, they are often confused and aligned to the wrong categories, which limits the generalization ability of the model across subjects. Besides, almost all methods only support offline applications, which require collecting a large number of samples of new subjects. To achieve online recognition, a simpler model is needed. In this paper, a novel Gated Recurrent Unit-Minimum Class Confusion (GRU-MCC) model is proposed. Specifically, a simple feature extractor based on gated recurrent unit (GRU) is firstly applied to model the spatial dependence of multiple electrodes and obtain high-level discriminative features. Then, during training, minimum class confusion (MCC) loss is introduced to reduce the confusion between the correct and ambiguous classes for the target subject and increase the transfer gains. We conduct both offline and online experiments on two public datasets: SEED and MPED. The results indicate that our method can obtain the superior performance.

US Guided vs Landmark Guided in Paramedian Spinal in Elderly

Abstract Background The ultrasound-assisted paramedian technique reduces the technical difficulty of spinal anaesthesia in the elderly. Compared with the landmark-guided paramedian technique, the use of ultrasound reduced the number of needle manipulations required for success, increased the firstattempt success rate and reduced block-associated pain and discomfort. Aim of the Work The study aims to evaluate whether ultrasound assisted paramedian spinal anaesthesia in the elderly can achieve successful dural puncture with fewer needle passes compared with the conventional landmark guided paramedian technique. Patients and Methods After obtaining approval from the Research Ethical Committee of Faculty of Medicine, Ain shams University; this study conducted in the operating theatres of Ain Shams University Hospitals with sample size of 10 patients / group will be needed. The patients randomly divided pre-operatively into two groups using their computer-generated random numbers such that „even numbers‟ will be enrolled in “group-L” for patients receive spinal anaesthesia by the paramedian approach by landmark guided palpation and „odd numbers‟ will be enrolled in “group-s” for patients ultrasound assisted approach. Results Ultrasonography might improve the efficacy of spinal anaesthesia by improving the accuracy of identification of intervertebral level, depth measurement of the intrathecal space and location of the interlaminar window that permits needle passage compared with the landmark guided technique, ultrasound assisted spinal anaesthesia decreases the number of puncture attempts and improves the success rate at the first attempt in patients with potential technical difficulties we believe that individualisation is a key element of minimising technical difficulties. Because of the large intersubject variability in anatomy, the blind technique have a theoretical upper limit of success rate. Through which we obtained success rates of 90 and 100% at the first attempt and within two attempts, respectively, in the ultrasound group. Conclusion Ultrasound-assisted paramedian spinal anaesthesia significantly reduces the number of needle manipulations required for success, increases the success rate at first pass and decreases periprocedural pain and patient discomfort compared with the landmark-guided paramedian technique in the elderly. Our results suggest that the neuraxial ultrasonography enhances the efficacy and comfort of spinal anaesthesia in the elderly.

Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation.

Accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to construct FBNs lack the ability to capture potential non-linear relationships between data and labels. Moreover, most existing studies treat the FBNs construction and disease classification as separate steps, leading to large inter-subject variability in the estimated FBNs and reducing the statistical power of subsequent group comparison. To address these limitations, we propose a new approach to FBNs construction called the deep unrolling-based spatial constraint representation (DUSCR) model and integrate it with a convolutional classifier to create an end-to-end framework for ASD recognition. Specifically, the model spatial constraint representation (SCR) is solved using a proximal gradient descent algorithm, and we unroll it into deep networks using the deep unrolling algorithm. Classification is then performed using a convolutional prototype learning model. We evaluated the effectiveness of the proposed method on the ABIDE I dataset and observed a significant improvement in model performance and classification accuracy. The resting state fMRI images are preprocessed into time series data and 3D coordinates of each region of interest. The data are fed into the DUSCR model, a model for building functional brain networks using deep learning instead of traditional models, that we propose, and then the outputs are fed into the convolutional classifier with prototype learning to determine whether the patient has ASD disease.

A computational model to simulate spectral modulation and speech perception experiments of cochlear implant users.

Speech understanding in cochlear implant (CI) users presents large intersubject variability that may be related to different aspects of the peripheral auditory system, such as the electrode-nerve interface and neural health conditions. This variability makes it more challenging to proof differences in performance between different CI sound coding strategies in regular clinical studies, nevertheless, computational models can be helpful to assess the speech performance of CI users in an environment where all these physiological aspects can be controlled. In this study, differences in performance between three variants of the HiRes Fidelity 120 (F120) sound coding strategy are studied with a computational model. The computational model consists of (i) a processing stage with the sound coding strategy, (ii) a three-dimensional electrode-nerve interface that accounts for auditory nerve fiber (ANF) degeneration, (iii) a population of phenomenological ANF models, and (iv) a feature extractor algorithm to obtain the internal representation (IR) of the neural activity. As the back-end, the simulation framework for auditory discrimination experiments (FADE) was chosen. Two experiments relevant to speech understanding were performed: one related to spectral modulation threshold (SMT), and the other one related to speech reception threshold (SRT). These experiments included three different neural health conditions (healthy ANFs, and moderate and severe ANF degeneration). The F120 was configured to use sequential stimulation (F120-S), and simultaneous stimulation with two (F120-P) and three (F120-T) simultaneously active channels. Simultaneous stimulation causes electric interaction that smears the spectrotemporal information transmitted to the ANFs, and it has been hypothesized to lead to even worse information transmission in poor neural health conditions. In general, worse neural health conditions led to worse predicted performance; nevertheless, the detriment was small compared to clinical data. Results in SRT experiments indicated that performance with simultaneous stimulation, especially F120-T, were more affected by neural degeneration than with sequential stimulation. Results in SMT experiments showed no significant difference in performance. Although the proposed model in its current state is able to perform SMT and SRT experiments, it is not reliable to predict real CI users' performance yet. Nevertheless, improvements related to the ANF model, feature extraction, and predictor algorithm are discussed.

Pediatric abusive head trauma: visual outcomes, evoked potentials, diffusion tensor imaging, and relationships to retinal hemorrhages

PurposeFunction and anatomy of the visual system were evaluated in children with abusive head trauma (AHT). The relationships between retinal hemorrhages at presentation were examined with outcome measures.MethodsRetrospective review of data in children with AHT for 1) visual acuity at last follow-up, 2) visual evoked potentials (VEP) after recovery, 3) diffusion metrics of white matter tracts and grey matter within the occipital lobe on diffusion tensor imaging (DTI), and 4) patterns of retinal hemorrhages at presentation. Visual acuity was converted into logarithm of minimum angle of resolution (logMAR) after correction for age. VEPs were also scored by objective signal-to-noise ratio (SNR).ResultsOf 202 AHT victims reviewed, 45 met inclusion criteria. Median logMAR was reduced to 0.8 (approximately 20/125 Snellen equivalent), with 27% having no measurable vision. Thirty-two percent of subjects had no detectable VEP signal. VEPs were significantly reduced in subjects initially presenting with traumatic retinoschisis or hemorrhages involving the macula (p < 0.01). DTI tract volumes were decreased in AHT subjects compared to controls (p < 0.001). DTI metrics were most affected in AHT victims showing macular abnormalities on follow-up ocular examination. However, DTI metrics were not correlated with visual acuity or VEPS. There was large inter-subject variability within each grouping.DiscussionMechanisms causing traumatic retinoschisis, or traumatic abnormalities of the macula, are associated with significant long-term visual pathway dysfunction. AHT associated abnormalities of the macula, and visual cortical pathways were more fully captured by VEPs than visual acuity or DTI metrics.

Motor Unit Number Estimation in Spastic Biceps Brachii Muscles of Chronic Stroke Survivors Before and After BoNT Injection.

The study aims to characterize the motor unit (MU) loss in spastic biceps brachii muscle (BBM) of chronic stroke survivors before and after botulinum neurotoxin (BoNT) injection. High-density weighted average (HDWA) motor unit number estimation (MUNE) was employed to estimate the number of functioning motor units of BBMs of eight chronic stroke survivors 1-week before (1st visit) and 3-week after (2nd visit) BoNT injection based on the surface electromyography (sEMG) signals recorded during voluntary contraction and supramaximal electrical stimulation. Significant lower MUNE was estimated from the spastic BBMs compared to the non-spastic MUNEs during two visits. A surprisingly higher MUNE was obtained from the spastic side during the 2nd visit after BoNT injection. The HDWA MUNE technique can be employed to characterize the motor unit loss in spastic muscle caused by upper motor neuro lesions at contraction level up to 30% MVC, but may fail to detect the MU loss caused by the chemodenervation effect of BoNT due to the non-uniform denervation of small and large size MUs. This study presents the first effort to evaluate the applicability of HDWA MUNE technique to characterize the MU loss in the spastic muscle following stroke and the subsequent BoNT injection for the treatment of post-stroke spasticity. The finding of this study suggests that HDWA MUNE can be a sensitive approach to detect the MU loss in spastic muscles after stroke, but the large inter-subject MUNE variability after the BoNT injection should be interpreted with caution.

Externalization of speech through hearing aids differing in microphone position and dome type

An experiment was conducted to examine how different hearing aid styles affect the externalization of speech. Of particular interest were effects of microphone position (behind-the-ear versus in-the-canal) and dome type (closed versus open). Participants were young adults with and without hearing loss, who were fitted with hearing aids that allowed variations in the microphone position and the dome type. They were seated in a large sound-treated booth and presented with monosyllabic words from loudspeakers at a distance of 1.5 m. Their task was to rate the perceived externalization of each word using a rating scale that ranged from 10 (at the loudspeaker in front) to 0 (in-the-head) to −10 (behind the listener). We observed a large intersubject variability in ratings. On average, compared to unaided listening, hearing aids tended to reduce perceived distance and lead to more in-the-head responses. This was especially true for closed domes in combination with behind-the-ear microphones. We also observed a strong association between poor externalization and front-back confusions.

Effect of low-volume blood withdrawal on hematological biomarkers

Introduction&#x0D; The hematological module of the Athlete Biological Passport (ABP) consists of individual and longitudinal monitoring of hematological parameters biomarkers responsive to erythropoiesis alteration. Based on an adaptive Bayesian model, the ABP seeks to identify non-physiological variations that may correspond to different types of blood doping. In this context, the impact of a standard blood donation (500 ml) was evident on the ABP parameters, hence supporting the robustness of the model. However, there is only scarce evidence of single or multiple withdrawals of smaller volumes of blood, closer to a modern blood doping strategy. This study investigated the impact of low-volume blood withdrawal on hematological variables for anti-doping purposes.&#x0D; Methods&#x0D; Twelve healthy subjects (7 women and 5 men; 26 ±6 yrs, 170 ±7 cm; 65 ±7 kg) were recruited. After baseline measurement, 140 ml whole blood was withdrawn with subsequent measures of hematological parameters by flow cytometry (Sysmex XN-1000, Sysmex Corporation, Japan) for 3 weeks. Blood volumes (BV) and total hemoglobin mass (Hbmass) were determined by means of a validated CO-rebreathing method. Ferritin (FERR) concentration was additionally measured by chemifluminescence immunoassay technique. Individual profiles were generated using the official ABP adaptive model using the official software platform of the World Anti-Doping Agency (WADA). Mixed model repeated measures analyses were run for each parameter to determine whether changes in the dependent (hematological variables) variables differed over time (fixed factor).&#x0D; Results&#x0D; 43.2 ±8 g of hemoglobin was removed during the 140 ml blood withdrawal. A significant decrease in Hbmass (726 ±185 vs. 709 ±189 g; p = 0.007) and Red Blood Cell Volume (RBCV; 2190 ±521 vs. 2129 ±552 L; p = 0.028) was observed at D + 7 with a return to basal value at D + 14. No significant variations were observed for the primary and secondary variables of the ABP. No Atypical Passport Finding (ATPF) were generated in the individual ABP profiles. Conversely, FERR was significantly downregulated at D + 7 (95.8 ±76 vs. 87.1 ±76 μg/l; p = 0.018).&#x0D; Discussion/Conclusion&#x0D; While the ABP has demonstrated substantial advantages for traditional doping scenarios, our results challenge the indirect detection of low-volume blood withdrawals in a contemporary blood doping scenario. The impact of low-volume autologous blood transfusion on time trial performance has recently been demonstrated, supporting the need for novel biomarkers with extended detection windows. Hbmass could hence represent an additional marker to screen blood doping while technical issues for the use of CO-rebreathing remain. FERR as a marker of altered iron metabolism was there against shown to be sensitive and possibly suitable for outlining low-volume blood transfusion. The large inter-subject variability underlines the importance of individual longitudinal monitoring to provide sufficient sensitivity in the interpretation of hematological variations. Our study finally confirms that even low-volume blood withdrawals are associated with significant hematological changes while frequent blood samples and additional biomarkers are certainly required to better tackle blood doping strategies using such low-volume transfusions.

Continual Learning of a Transformer-Based Deep Learning Classifier Using an Initial Model from Action Observation EEG Data to Online Motor Imagery Classification.

The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that enables users to communicate with external environments through their minds. However, current MI-BCI systems ask naïve subjects to perform unfamiliar MI tasks with simple textual instruction or a visual/auditory cue. The unclear instruction for MI execution not only results in large inter-subject variability in the measured EEG patterns but also causes the difficulty of grouping cross-subject data for big-data training. In this study, we designed an BCI training method in a virtual reality (VR) environment. Subjects wore a head-mounted device (HMD) and executed action observation (AO) concurrently with MI (i.e., AO + MI) in VR environments. EEG signals recorded in AO + MI task were used to train an initial model, and the initial model was continually improved by the provision of EEG data in the following BCI training sessions. We recruited five healthy subjects, and each subject was requested to participate in three kinds of tasks, including an AO + MI task, an MI task, and the task of MI with visual feedback (MI-FB) three times. This study adopted a transformer- based spatial-temporal network (TSTN) to decode the user's MI intentions. In contrast to other convolutional neural network (CNN) or recurrent neural network (RNN) approaches, the TSTN extracts spatial and temporal features, and applies attention mechanisms along spatial and temporal dimensions to perceive the global dependencies. The mean detection accuracies of TSTN were 0.63, 0.68, 0.75, and 0.77 in the MI, first MI-FB, second MI-FB, and third MI-FB sessions, respectively. This study demonstrated the AO + MI gave an easier way for subjects to conform their imagery actions, and the BCI performance was improved with the continual learning of the MI-FB training process.

Body mass index is not suitable for assessing body composition in children with spastic cerebral palsy

Purpose To measure body composition by using bioelectrical impedance analysis (BIA) and body mass index (BMI) and to investigate the correlation and agreement between BMI and fat mass percentage in children with spastic Cerebral Palsy. Materials and methods BIA was used to assess fat mass percentage and BMI was determined from body weight and height. BMI and fat mass percentage were both categorized into five categories. The association between fat mass percentage and BMI was assessed using Pearson’s correlation coefficient. Agreement between BMI and fat mass percentage was investigated with weighted Cohen’s kappa coefficient. Results One hundred and three children with CP across all Gross Motor Function Classification Levels (61% boys, mean age 9 years) were included. Mean BMI was 18.3 kg/m2 and mean fat mass was 24.9%. A large inter-subject variability was found with a weak correlation between BMI and fat mass percentage in children with a BMI < 20 kg/m2. Little agreement (k = 0.299, CI 0.16–0.44) between the categorization of children based on BMI and based on fat mass percentage was found. Interpretation The large inter-subject variability in fat mass percentage combined with little agreement between the BMI and BIA categories suggests that BMI is not a suitable measure of fat mass in children with CP. IMPLICATIONS FOR REHABILITATION Using body mass index (BMI) and instead of fat mass percentage increases the risk of misclassifying body composition in children with spastic Cerebral Palsy. Children with a BMI < 20 kg/m2 are more at risk to be misclassified for body composition.

In Silico Quantification of Intersubject Variability on Aerosol Deposition in the Oral Airway

The extrathoracic oral airway is not only a major mechanical barrier for pharmaceutical aerosols to reach the lung but also a major source of variability in lung deposition. Using computational fluid dynamics, deposition of 1−30 µm particles was predicted in 11 CT-based models of the oral airways of adults. Simulations were performed for mouth breathing during both inspiration and expiration at two steady-state flow rates representative of resting/nebulizer use (18 L/min) and of dry powder inhaler (DPI) use (45 L/min). Consistent with previous in vitro studies, there was a large intersubject variability in oral deposition. For an optimal size distribution of 1−5 µm for pharmaceutical aerosols, our data suggest that >75% of the inhaled aerosol is delivered to the intrathoracic lungs in most subjects when using a nebulizer but only in about half the subjects when using a DPI. There was no significant difference in oral deposition efficiency between inspiration and expiration, unlike subregional deposition, which shows significantly different patterns between the two breathing phases. These results highlight the need for incorporating a morphological variation of the upper airway in predictive models of aerosol deposition for accurate predictions of particle dosimetry in the intrathoracic region of the lung.

MCGRAM: Linking Multi-Scale CNN With a Graph-Based Recurrent Attention Model for Subject-Independent ERP Detection

Electroencephalogram (EEG)-based event-related potential (ERP) detection is expected to play an important role in many applications. However, limited by the low signal-to-noise ratio and large inter-subject variability of EEG signals, it is still challenging to design a high-precision ERP detection method in subject-independent scenarios. In this brief, a novel graph-based multi-scale convolutional recurrent attention model (MCGRAM) is proposed to extract the underlying invariant features of EEG signals for subject-independent ERP detection. Specifically, considering the frequency factors of ERP signals, the multi-scale convolution module is designed for learning the frequency representations. Then, the graph convolution module is developed by constructing the links between each EEG node and designing trainable weights of the links to extract the discriminative spatial features. Besides, two-layer LSTM combined with a self-attention model is utilized to extract temporal representations of different EEG slices. Compared to the existing state-of-the-art methods, MCGRAM achieves the best performance in terms of AUC and F1-Score on a rapid serial visual presentation benchmark dataset. Lastly, the ablation studies demonstrate the superiority of each component in MCGRAM.

Effects of nasal anatomical characteristics and administration parameters on delivery of locally-acting drugs with suspension nasal sprays in adults

Large intersubject variability in local drug delivery to the target regions in the nose, the region posterior to the internal nasal valve (INV), was previously demonstrated in vitro by Manniello et al. (2021), using nasal airway models from a diverse group of adults (n = 20; 21–75 years old, 50% female and 50% ≥ 50 years old) across a number of suspension nasal drug products. Upon quantification of the range of posterior deposition (PD), three nasal models representing minimum, mean, and maximum PD were previously selected by Alfaifi et al. (2022) to represent a reasonable degree of intersubject variability for nasal suspension drug products. This study investigated whether any relationship exists between the anatomical characteristics of the nasal airways and PD. Anatomical characteristics of 20 healthy adult nasal airways were measured and their relationships with previously measured in vitro PD data with fluticasone propionate (FP) and fluticasone furoate (FF) nasal spray drug products, respectively, were evaluated. The stepwise regression method was used to automatically select the potential PD predictor variables. No individual anatomical or device-related airway dimensions showed a trend or strong correlation with drug PD. A strong correlation (r2 = 0.91) was found between the PD of the FP nasal spray and a combination of main effects and two-way interactions of ten variables, including some of the dimensions of the nasal airways and administration parameters. Similarly, a correlation was found between the PD of the FF nasal spray and six parameters, although the strength of the correlation (r2 = 0.61) was less compared with the correlation for FP (r2 = 0.91). The complexity of the identified correlations supports the continued development and use of characteristic in vitro nasal geometries and accurate CFD methods to predict regional nasal spray deposition across a wide range of subjects and spray products.

Intersubject Variability in Cerebrovascular Hemodynamics and Systemic Physiology during a Verbal Fluency Task under Colored Light Exposure: Clustering of Subjects by Unsupervised Machine Learning.

There is large intersubject variability in cerebrovascular hemodynamic and systemic physiological responses induced by a verbal fluency task (VFT) under colored light exposure (CLE). We hypothesized that machine learning would enable us to classify the response patterns and provide new insights into the common response patterns between subjects. In total, 32 healthy subjects (15 men and 17 women, age: 25.5 ± 4.3 years) were exposed to two different light colors (red vs. blue) in a randomized cross-over study design for 9 min while performing a VFT. We used the systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS) approach to measure cerebrovascular hemodynamics and oxygenation at the prefrontal cortex (PFC) and visual cortex (VC) concurrently with systemic physiological parameters. We found that subjects were suitably classified by unsupervised machine learning into different groups according to the changes in the following parameters: end-tidal carbon dioxide, arterial oxygen saturation, skin conductance, oxygenated hemoglobin in the VC, and deoxygenated hemoglobin in the PFC. With hard clustering methods, three and five different groups of subjects were found for the blue and red light exposure, respectively. Our results highlight the fact that humans show specific reactivity types to the CLE-VFT experimental paradigm.

Tumour, whole-blood, plasma and tissue concentrations of metformin in lung cancer patients.

Metformin is used for the management of type 2 diabetes mellitus (T2DM) and is being tested clinically as an anticancer agent. Metformin concentrations safely achievable in human solid tissues including tumours are unknown. This study was designed to determine metformin concentration in tissue compartments as a function of dose to inform rational dosing in preclinical models and interpretation of clinical results." Subjects with solid tumours to be treated by resection and either (A) willingness to take metformin for 7-10 days before surgery or (B) taking metformin for T2DM were eligible. Whole blood, plasma, tumour, tumour-adjacent uninvolved tissue and subcutaneous adipose tissue were obtained for liquid chromatography with tandem mass spectrometry to measure metformin concentrations. All subjects had primary lung tumours. Metformin dose was significantly correlated with drug concentrations in all tissues analysed. Intersubject metformin concentrations varied by over two orders of magnitude. Metformin concentrations were significantly higher in tumour tissues and lower in adipose tissues compared to other tissues. Concentrations in blood and plasma were significantly correlated with concentrations in solid tissues. Metformin accumulates in cellular compartments. Concentrations observed in plasma, blood, lung and tumour tissues in subjects treated with US Food and Drug Administration-approved doses for T2DM are lower than those typically used in tissue culture studies. However, such tissue concentrations are in line with those found within cultured cells treated with supra-pharmacological doses of metformin. Given the large intersubject variability in metformin concentrations, it is imperative to determine whether there is an association between tissue metformin concentration and anticancer activity in humans.

Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification.

Transient oscillatory events in the sleep electroencephalogram represent short-term coordinated network activity. Of particular importance, sleep spindles are transient oscillatory events associated with memory consolidation, which are altered in aging and in several psychiatric and neurodegenerative disorders. Spindle identification, however, currently contains implicit assumptions derived from what waveforms were historically easiest to discern by eye, and has recently been shown to select only a high-amplitude subset of transient events. Moreover, spindle activity is typically averaged across a sleep stage, collapsing continuous dynamics into discrete states. What information can be gained by expanding our view of transient oscillatory events and their dynamics? In this paper, we develop a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events across a wide frequency range using continuous dynamics. We demonstrate that the complex temporal evolution of transient events during sleep is highly stereotyped when viewed as a function of slow oscillation power (an objective, continuous metric of depth-of-sleep) and phase (a correlate of cortical up/down states). This two-fold power-phase representation has large intersubject variability-even within healthy controls-yet strong night-to-night stability for individuals, suggesting a robust basis for phenotyping. As a clinical application, we then analyze patients with schizophrenia, confirming established spindle (12-15 Hz) deficits as well as identifying novel differences in transient non-rapid eye movement events in low-alpha (7-10 Hz) and theta (4-6 Hz) ranges. Overall, these results offer an expanded view of transient activity, describing a broad class of events with properties varying continuously across spatial, temporal, and phase-coupling dimensions.

The Intelligibility of Time-Compressed Speech Is Correlated with the Ability to Listen in Modulated Noise.

Speech perception in difficult listening conditions depends highly on the temporal processing ability of the auditory pathway. In the present study, we investigate the inter-subject variability of young normal-hearing listeners in the identification of time-compressed speech and how the ability to identify time-compressed speech, as assessed by the speech reception threshold (SRTrate: the speech rate at which 50% of the speech is perceived correctly) is associated with the ability to identify speech in unmodulated (SRTunmod) and modulated noise (SRTmod). These tasks are highly dependent on the temporal processing abilities of the auditory pathway. We observed a large inter-subject variability in the SRTrate and found that it is significantly correlated with the SRT when listening to unmodulated and modulated noise. Furthermore, we found that listeners who are better at perceiving speech at high rates are better in listening to speech in modulated noise. This effect persisted even when controlling for their ability to perceive speech in unmodulated noise. In addition, we also found that an increase in speech rate from 2.7 to 6.6 syllables per second resulted in a reduction in glimpsing of 5.3dB when listening to speech in a 4-Hz amplitude-modulated masker, even though speech in quiet was 100% intelligible at both rates. These results indicate that the ability of young normal-hearing individuals to efficiently process temporal features of speech is an imperative factor when listening to speech in difficult listening situations.