BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development

The benefits, opportunities and growing availability of ultra-high field magnetic resonance imaging (MRI) for humans have prompted an expansion in research and development efforts towards increasingly more advanced high-resolution imaging techniques. To maximize their effectiveness, these efforts need to be supported by powerful computational simulation platforms that can adequately reproduce the biophysical characteristics of MRI, with high spatial resolution. In this work, we have sought to address this need by developing a novel digital phantom with realistic anatomical detail up to 100-µm resolution, including multiple MRI properties that affect image generation. This phantom, termed BigBrain-MR, was generated from the publicly available BigBrain histological dataset and lower-resolution in-vivo 7T-MRI data, using a newly-developed image processing framework that allows mapping the general properties of the latter into the fine anatomical scale of the former. Overall, the mapping framework was found to be effective and robust, yielding a diverse range of realistic "in-vivo-like" MRI contrasts and maps at 100-µm resolution. BigBrain-MR was then tested in three imaging applications (motion effects and interpolation, super-resolution imaging, and parallel imaging reconstruction) to investigate its properties, value and validity as a simulation platform. The results consistently showed that BigBrain-MR can closely approximate the behavior of real in-vivo data, more realistically and with more extensive features than a more classic option such as the Shepp-Logan phantom. Its flexibility in simulating different contrast mechanisms and artifacts may also prove valuable for educational applications. BigBrain-MR is therefore deemed a favorable choice to support methodological development and demonstration in brain MRI, and has been made freely available to the community.

Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning

Blood pressure (BP) is a crucial biomarker giving valuable information regarding cardiovascular diseases but requires accurate continuous monitoring to maximize its value. In the effort of developing non-invasive, non-occlusive and continuous BP monitoring devices, photoplethysmography (PPG) has recently gained interest. Researchers have attempted to estimate BP based on the analysis of PPG waveform morphology, with promising results, yet often validated on a small number of subjects with moderate BP variations. This work presents an accurate BP estimator based on PPG morphology features. The method first uses a clinically-validated algorithm (oBPM®) to perform signal preprocessing and extraction of physiological features. A subset of features that best reflects BP changes is automatically identified by Lasso regression, and a feature relevance analysis is conducted. Three machine learning (ML) methods are then investigated to translate this subset of features into systolic BP (SBP) and diastolic BP (DBP) estimates; namely Lasso regression, support vector regression and Gaussian process regression. The accuracy of absolute BP estimates and trending ability are evaluated. Such an approach considerably improves the performance for SBP estimation over previous oBPM® technology, with a reduction in the standard deviation of the error of over 20%. Furthermore, rapid BP changes assessed by the PPG-based approach demonstrates concordance rate over 99% with the invasive reference. Altogether, the results confirm that PPG morphology features can be combined with ML methods to accurately track BP variations generated during anesthesia induction. They also reinforce the importance of adding a calibration measure to obtain an absolute BP estimate.

Spring and latch dynamics can act as control pathways in ultrafast systems

Ultrafast movements propelled by springs and released by latches are thought limited to energetic adjustments prior to movement, and seemingly cannot adjust once movement begins. Even so, across the tree of life, ultrafast organisms navigate dynamic environments and generate a range of movements, suggesting unrecognized capabilities for control. We develop a framework of control pathways leveraging the non-linear dynamics of spring-propelled, latch-released systems. We analytically model spring dynamics and develop reduced-parameter models of latch dynamics to quantify how they can be tuned internally or through changing external environments. Using Lagrangian mechanics, we test feedforward and feedback control implementation via spring and latch dynamics. We establish through empirically-informed modeling that ultrafast movement can be controllably varied during latch release and spring propulsion. A deeper understanding of the interconnection between multiple control pathways, and the tunability of each control pathway, in ultrafast biomechanical systems presented here has the potential to expand the capabilities of synthetic ultra-fast systems and provides a new framework to understand the behaviors of fast organisms subject to perturbations and environmental non-idealities.

Contactless skin perfusion monitoring with video cameras: tracking pharmacological vasoconstriction and vasodilation using photoplethysmographic changes

Objectives.Clinical assessment of skin perfusion informs prognosis in critically ill patients. Video camera monitoring could provide an objective, continuous method to monitor skin perfusion. In this prospective, interventional study of healthy volunteers, we tested whether video camera-derived photoplethysmography imaging and colour measurements could detect drug-induced skin perfusion changes.Approach.We monitored the lower limbs of 30 volunteers using video cameras while administering phenylephrine (a vasoconstrictor) and glyceryl trinitrate (a vasodilator). We report relative pixel intensity changes from baseline, as absolute values are sensitive to environmental factors. The primary outcome was the pre- to peak- infusion green channel amplitude change in the pulsatile PPGi waveform component. Secondary outcomes were pre-to-peak changes in the photoplethysmographic imaging waveform baseline, skin colour hue and skin colour saturation.Main results.The 30 participants had a median age of 29 years (IQR 25-34), sixteen (53%) were male. A 34.7% (p= 0.0001) mean decrease in the amplitude of the pulsatile photoplethysmographic imaging waveform occurred following phenylephrine infusion. A 30.7% (p= 0.000004) mean increase occurred following glyceryl trinitrate infusion. The photoplethysmographic imaging baseline decreased with phenylephrine by 2.1% (p= 0.000 02) and increased with glyceryl trinitrate by 0.5% (p= 0.026). Skin colour hue changed in opposite direction with phenylephrine (-0.0013,p= 0.0002) and glyceryl trinitrate (+0.0006,p= 0.019). Skin colour saturation decreased with phenylephrine by 0.0022 (p= 0.0002), with no significant change observed with glyceryl trinitrate (+0.0005,p= 0.21).Significance.Drug-induced vasoconstriction and vasodilation are associated with detectable changes in photoplethysmographic imaging waveform parameters and skin hue. Our findings suggest video cameras have great potential for continuous, contactless skin perfusion monitoring.

Segmenting electroencephalography wires reduces radiofrequency shielding artifacts in simultaneous electroencephalography and functional magnetic resonance imaging at 7 T

Purpose

Simultaneous scalp electroencephalography and functional magnetic resonance imaging (EEG‐fMRI) enable noninvasive assessment of brain function with high spatial and temporal resolution. However, at ultra‐high field, the data quality of both modalities is degraded by mutual interactions. Here, we thoroughly investigated the radiofrequency (RF) shielding artifact of a state‐of‐the‐art EEG‐fMRI setup, at 7 T, and design a practical solution to limit this issue.

Methods

Electromagnetic field simulations and MR measurements assessed the shielding effect of the EEG setup, more specifically the EEG wiring. The effectiveness of segmenting the wiring with resistors to reduce the transmit field disruption was evaluated on a wire‐only EEG model and a simulation model of the EEG cap.

Results

The EEG wiring was found to exert a dominant effect on the disruption of the transmit field, whose intensity varied periodically as a function of the wire length. Breaking the electrical continuity of the EEG wires into segments shorter than one quarter RF wavelength in air (25 cm at 7 T) reduced significantly the RF shielding artifacts. Simulations of the EEG cap with segmented wires indicated similar improvements for a moderate increase of the power deposition.

Conclusion

We demonstrated that segmenting the EEG wiring into shorter lengths using commercially available nonmagnetic resistors is effective at reducing RF shielding artifacts in simultaneous EEG‐fMRI. This prevents the formation of RF‐induced standing waves, without substantial specific absorption rate (SAR) penalties, and thereby enables benefiting from the functional sensitivity boosts achievable at ultra‐high field.

New method to measure interbreath intervals in infants for the assessment of apnoea and respiration

Background

Respiratory disorders, including apnoea, are common in preterm infants due to their immature respiratory control compared with term-born infants. However, our inability to accurately measure respiratory rate in hospitalised infants results in unreported episodes of apnoea and an incomplete picture of respiratory activity.

Methods

We develop, validate and use a novel algorithm to identify interbreath intervals (IBIs) and apnoeas in preterm infants. In 42 preterm infants (1600 hours of recordings), we assess IBIs from the chest electrical impedance pneumograph using an adaptive amplitude threshold for the detection of breaths. The algorithm is refined by comparing its accuracy with clinically observed breaths and pauses in breathing. We develop an automated classifier to differentiate periods of true apnoea from artefactually low amplitude signal. We assess the performance of this algorithm in the detection of morphine-induced respiratory depression. Finally, we use the algorithm to investigate whether retinopathy of prematurity (ROP) screening alters the IBI distribution.

Results

Individual breaths were detected with a false-positive rate of 13% and a false-negative rate of 12%. The classifier identified true apnoeas with an accuracy of 93%. As expected, morphine caused a significant shift in the IBI distribution towards longer IBIs. Following ROP screening, there was a significant increase in pauses in breathing that lasted more than 10 s (t-statistic=1.82, p=0.023). This was not reflected by changes in the monitor-derived respiratory rate and no episodes of apnoea were recorded in the medical records.

Conclusions

We show that our algorithm offers an improved method for the identification of IBIs and apnoeas in preterm infants. Following ROP screening, increased respiratory instability can occur even in the absence of clinically significant apnoeas. Accurate assessment of infant respiratory activity is essential to inform clinical practice.

Pulse arrival time as a surrogate of blood pressure

Various models have been proposed for the estimation of blood pressure (BP) from pulse transit time (PTT). PTT is defined as the time delay of the pressure wave, produced by left ventricular contraction, measured between a proximal and a distal site along the arterial tree. Most researchers, when they measure the time difference between the peak of the R-wave in the electrocardiogram signal (corresponding to left ventricular depolarisation) and a fiducial point in the photoplethysmogram waveform (as measured by a pulse oximeter attached to the fingertip), describe this erroneously as the PTT. In fact, this is the pulse arrival time (PAT), which includes not only PTT, but also the time delay between the electrical depolarisation of the heart's left ventricle and the opening of the aortic valve, known as pre-ejection period (PEP). PEP has been suggested to present a significant limitation to BP estimation using PAT. This work investigates the impact of PEP on PAT, leading to a discussion on the best models for BP estimation using PAT or PTT. We conducted a clinical study involving 30 healthy volunteers (53.3% female, 30.9 ± 9.35 years old, with a body mass index of 22.7 ± 3.2 kg/m[Formula: see text]). Each session lasted on average 27.9 ± 0.6 min and BP was varied by an infusion of phenylephrine (a medication that causes venous and arterial vasoconstriction). We introduced new processing steps for the analysis of PAT and PEP signals. Various population-based models (Poon, Gesche and Fung) and a posteriori models (inverse linear, inverse squared and logarithm) for estimation of BP from PTT or PAT were evaluated. Across the cohort, PEP was found to increase by 5.5 ms ± 4.5 ms from its baseline value. Variations in PTT were significantly larger in amplitude, - 16.8 ms ± 7.5 ms. We suggest, therefore, that for infusions of phenylephrine, the contribution of PEP on PAT can be neglected. All population-based models produced large BP estimation errors, suggesting that they are insufficient for modelling the complex pathways relating changes in PTT or PAT to changes in BP. Although PAT is inversely correlated with systolic blood pressure (SBP), the gradient of this relationship varies significantly from individual to individual, from - 2946 to - 470.64 mmHg/s in our dataset. For the a posteriori inverse squared model, the root mean squared errors (RMSE) for systolic and diastolic blood pressure (DBP) estimation from PAT were 5.49 mmHg and 3.82 mmHg, respectively. The RMSEs for SBP and DBP estimation by PTT were 4.51 mmHg and 3.53 mmHg, respectively. These models take into account individual calibration curves required for accurate blood pressure estimation. The best performing population-based model (Poon) reported error values around double that of the a posteriori inverse squared model, and so the use of population-based models is not justified.

Oral pigmented lesions: a retrospective analysis from Brazil

Background

Pigmented lesions are uncommon in the oral mucosa, and studies investigating the incidence and types of these lesions are desired to improve the diagnostic knowledge of clinicians. The aim of this study was to analyze the distribution of oral pigmented lesions in a Brazilian population.

Material and Methods

A retrospective descriptive cross-sectional study was performed. Oral pigmented lesions were retrieved from the files of two oral and maxillofacial pathology services from Brazil over a 45-year period (1974-2019). The clinical data and the diagnoses of each case were retrieved and included in a Microsoft Excel® database.

Results

From 77.074 lesions diagnosed in this period, 761 (0.99%) represented pigmented lesions of the oral mucosa, including 351 (46.1%) melanocytic and 410 (53.9%) non-melanocytic lesions, with a higher incidence in females (73.2%) between the fourth and seventh decades of life. Amalgam tattoo (53.6%) represented the most common lesion, followed by melanotic macule (18.3%) and racial pigmentation (10.8%). Other pigmented lesions included nevus (9.9%), post-inflammatory pigmentation (3%), melanoma (2.1%), melanoacanthoma (1.4%), smoker's melanosis (0.4%), drug-induced pigmentation (0.3%), and melanotic neuroectodermal tumor of infancy (0.1%). The buccal mucosa was the most commonly affected site (25.2%), followed by the alveolar ridge (14.5%), and gingiva (11.8%).

Conclusions

The current findings were similar to previous studies with minor differences due methodology and characteristics of the services from where lesions were retrieved. The knowledge of these data may contribute to a better understanding of oral pigmented lesions and assist clinicians to better recognize and manage them.

Key words:Pigmented lesions, pigmentation, melanin, amalgam, oral cavity.

Day-to-day progression of vital-sign circadian rhythms in the intensive care unit

Background

Disrupted vital-sign circadian rhythms in the intensive care unit (ICU) are associated with complications such as immune system disruption, delirium and increased patient mortality. However, the prevalence and extent of this disruption is not well understood. Tools for its detection are currently limited.

Methods

This paper evaluated and compared vital-sign circadian rhythms in systolic blood pressure, heart rate, respiratory rate and temperature. Comparisons were made between the cohort of patients who recovered from the ICU and those who did not, across three large, publicly available clinical databases. This comparison included a qualitative assessment of rhythm profiles, as well as quantitative metrics such as peak–nadir excursions and correlation to a demographically matched ‘recovered’ profile.

Results

Circadian rhythms were present at the cohort level in all vital signs throughout an ICU stay. Peak–nadir excursions and correlation to a ‘recovered’ profile were typically greater throughout an ICU stay in the cohort of patients who recovered, compared to the cohort of patients who did not.

Conclusions

These results suggest that vital-sign circadian rhythms are typically present at the cohort level throughout an ICU stay and that quantitative assessment of these rhythms may provide information of prognostic use in the ICU.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03574-w.

Evaluation of natural background levels of high mountain karst aquifers in complex hydrogeological settings. A Gaussian mixture model approach in the Port del Comte (SE, Pyrenees) case study

The hydrogeological processes driving the hydrochemical composition of groundwater in the alpine pristine aquifer system of the Port del Comte Massif (PCM) are characterized through the multivariate statistical techniques Principal Component Analysis (PCA) and Gaussian Mixture Models (GMM) in the framework of Compositional Data (CoDa) analysis. Also, the groundwater Natural Background Levels (NBLs) for NO₃ and SO₄ and Cl are evaluated, which are specially important for indicating the occurrence of groundwater contamination derived from the anthropic activities conducted in the PCM. The different hydrogeochemical facies found in the aquifer system of the PCM comprises low mineralized Ca-HCO₃ water for the main Eocene karst aquifer, and Ca-SO₄ and highly mineralized NaCl water types in the minor aquifers discharging from the PCM. The NBL values of SO₄, Cl and NO₃ obtained for the main karst aquifer are 14.33, 4.06 and 6.55 mg/L, respectively. These values are 35, 3 and 1.2 times lower than the respective official NBLs values that were determined by the water administration to be compared with in the case of conducting a pollution assessment characterization in the main karst aquifer. Official overestimation of NBLs can put important groundwater resources in the PCM at risk.

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r ≈ 0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.

Snaps of a tiny amphipod push the boundary of ultrafast, repeatable movement

Surprisingly, the fastest motions are not produced by large animals or robots. Rather, small organisms or structures, including cnidarian stinging cells, fungal shooting spores, and mandible strikes of ants, termites, and spiders, hold the world acceleration records.^1-5 These diverse systems share common features: they rapidly convert potential energy - stored in deformed material or fluid - into kinetic energy when a latch is released.^4-6 However, the fastest of these are not repeatable, because mechanical components are broken or ejected.^5,6 Furthermore, some of these systems must overcome the added challenge of moving in water, where high density and viscosity constrain acceleration at small sizes. Here we report the kinematics of repeatable, ultrafast snaps by tiny marine amphipods (Dulichiella cf. appendiculata). Males use their enlarged major claw, which can exceed 30% of body mass, to snap a 1 mm-long dactyl with a diameter equivalent to a human hair (184 μm). The claw snaps closed extremely rapidly, averaging 93 μs, 17 m s^-1, and 2.4 x 10⁵ m s^-2. These snaps are among the smallest and fastest of any documented repeatable movement, and are sufficiently fast to operate in the inertial hydrodynamic regime (Reynolds number (Re) >10,000). They generate audible pops and rapid water jets, which occasionally yield cavitation, and may be used for defense. These amphipod snaps push the boundaries of acceleration and size for repeatable movements, particularly in water, and exemplify how new biomechanical insights can arise from unassuming animals. VIDEO ABSTRACT.

EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States

Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.

Comparison of the whole slide imaging and conventional light microscopy in the grading of oral epithelial dysplasia: a multi-institutional study

Background

Whole Slide Imaging (WSI) is an alternative method to light microscopy (LM). However, few studies have compared the diagnostic agreement between WSI and LM, especially to grade oral epithelial dysplasia (OED). The purpose of this study was to evaluate the variability in grading OED by the World Health Organization grading system, using WSI and conventional LM, and to investigate whether the access to clinical information, and psychologic or physical states of the pathologists could interfere with the diagnosis.

Material and Methods

eleven experienced pathologists from seven Brazilian universities independently evaluated twenty-five OED cases. The analyses were performed in duplicate for each method, with an interval of at least 30 days, and the time consumed in each analysis was measured. Physical and psychologic states were evaluated by blood pressure levels, heart rate and two questionnaires: State-Trait Anxiety Inventory and Perceived Stress Scale. Clinical information was provided after the second evaluation using WSI and the pathologist could change their diagnostic decision or not.

Results

LM showed a higher inter-examiner agreement (k=0.53) than WSI (k=0.45) and a smaller time consumed by the pathologists (mean of 65.53 seconds compared to 91.02 seconds in WSI). In the first analysis using conventional microscopy, there was a positive correlation between kappa values and anxiety (r=0.47, p=0.02), and stress (r=0.64, p<0.01), and an inverse correlation with heart rate (r=-0.48, p=0.02). In the digital analysis, there was also a positive correlation between kappa values and anxiety (r=0.75, p<0.001). After clinical information was given, there was a slight change in 11.3% of the cases, and a great discrepancy in 1.1% of the cases, mainly increasing the OED grade.

Conclusions

both microscopy systems had similar results, although LM had slightly higher kappa values, and WSI was more time consuming.

Key words:Pathology, microscopy, diagnosis, leukoplakia oral, anxiety.

EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States

Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.

Comparative expression of cyclooxygenase 2 and Ki67 in amelanotic and conventional oral melanomas

BACKGROUND

Oral melanomas have some histopathological resemblance with its cutaneous counterpart; however, an aggressive behavior is more common in tumors that occur in the oral cavity. Several markers have been suggested as indicative of tumoral progression and aggressiveness, such as cyclooxygenase 2 (COX-2) and Ki67.

MATERIAL AND METHODS

In this study, we have compared the expression of COX-2 and Ki67 in a series of amelanotic (n=7) and melanotic oral melanomas (n=22). The cases were selected from 4 pathology laboratories and submitted to the immunohistochemical (IHC) reactions. We analyzed the IHC staining based on a qualitative - using visual scores; and a computer-assisted method (quantitative) using scanned slides and software for digital analysis.

RESULTS

COX-2 was expressed in all oral melanomas; however, its intensity was significantly higher in the amelanotic ones (P<0.001). Similarly, a high Ki67-positivity index was observed in the amelanotic than melanotic ones (P<0.001).

CONCLUSIONS

Based on these results, we suggest that amelanotic oral melanomas have marked pro-inflammatory and high-proliferative phenotype, justifying their more aggressive behavior compared with the melanotic ones.

Non-contact vital-sign monitoring of patients undergoing haemodialysis treatment

A clinical study was designed to record a wide range of physiological values from patients undergoing haemodialysis treatment in the Renal Unit of the Churchill Hospital in Oxford. Video was recorded for a total of 84 dialysis sessions from 40 patients during the course of 1 year, comprising an overall video recording time of approximately 304.1 h. Reference values were provided by two devices in regular clinical use. The mean absolute error between the heart rate estimates from the camera and the average from two reference pulse oximeters (positioned at the finger and earlobe) was 2.8 beats/min for over 65% of the time the patient was stable. The mean absolute error between the respiratory rate estimates from the camera and the reference values (computed from the Electrocardiogram and a thoracic expansion sensor-chest belt) was 2.1 breaths/min for over 69% of the time for which the reference signals were valid. To increase the robustness of the algorithms, novel methods were devised for cancelling out aliased frequency components caused by the artificial light sources in the hospital, using auto-regressive modelling and pole cancellation. Maps of the spatial distribution of heart rate and respiratory rate information were developed from the coefficients of the auto-regressive models. Most of the periods for which the camera could not produce a reliable heart rate estimate lasted under 3 min, thus opening the possibility to monitor heart rate continuously in a clinical environment.

3-Dimensional magnetic resonance imaging of the freely moving human eye

Eye motion is a major confound for magnetic resonance imaging (MRI) in neuroscience or ophthalmology. Currently, solutions toward eye stabilisation include participants fixating or administration of paralytics/anaesthetics. We developed a novel MRI protocol for acquiring 3-dimensional images while the eye freely moves. Eye motion serves as the basis for image reconstruction, rather than an impediment. We fully reconstruct videos of the moving eye and head. We quantitatively validate data quality with millimetre resolution in two ways for individual participants. First, eye position based on reconstructed images correlated with simultaneous eye-tracking. Second, the reconstructed images preserve anatomical properties; the eye's axial length measured from MRI images matched that obtained with ocular biometry. The technique operates on a standard clinical setup, without necessitating specialized hardware, facilitating wide deployment. In clinical practice, we anticipate that this may help reduce burdens on both patients and infrastructure, by integrating multiple varieties of assessments into a single comprehensive session. More generally, our protocol is a harbinger for removing the necessity of fixation, thereby opening new opportunities for ethologically-valid, naturalistic paradigms, the inclusion of populations typically unable to stably fixate, and increased translational research such as in awake animals whose eye movements constitute an accessible behavioural readout.

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis

The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a deep learning-based prototype for automated assessment of the CVS in white matter MS lesions using data from three different imaging centers. We retrospectively analyzed data from 3 T magnetic resonance images acquired on four scanners from two different vendors, including adults with MS (n = 42), MS mimics (n = 33, encompassing 12 distinct neurological diseases mimicking MS) and uncertain diagnosis (n = 5). Brain white matter lesions were manually segmented on FLAIR* images. Perivenular assessment was performed according to consensus guidelines and used as ground truth, yielding 539 CVS-positive (CVS⁺ ) and 448 CVS-negative (CVS^- ) lesions. A 3D convolutional neural network ("CVSnet") was designed and trained on 47 datasets, keeping 33 for testing. FLAIR* lesion patches of CVS⁺ /CVS^- lesions were used for training and validation (n = 375/298) and for testing (n = 164/150). Performance was evaluated lesion-wise and subject-wise and compared with a state-of-the-art vesselness filtering approach through McNemar's test. The proposed CVSnet approached human performance, with lesion-wise median balanced accuracy of 81%, and subject-wise balanced accuracy of 89% on the validation set, and 91% on the test set. The process of CVS assessment, in previously manually segmented lesions, was ~ 600-fold faster using the proposed CVSnet compared with human visual assessment (test set: 4 seconds vs. 40 minutes). On the validation and test sets, the lesion-wise performance outperformed the vesselness filter method (P < 0.001). The proposed deep learning prototype shows promising performance in differentiating MS from its mimics. Our approach was evaluated using data from different hospitals, enabling larger multicenter trials to evaluate the benefit of introducing the CVS marker into MS diagnostic criteria.

Improved susceptibility-weighted imaging for high contrast and resolution thalamic nuclei mapping at 7T

PURPOSE

The thalamus is an important brain structure and neurosurgical target, but its constituting nuclei are challenging to image non-invasively. Recently, susceptibility-weighted imaging (SWI) at ultra-high field has shown promising capabilities for thalamic nuclei mapping. In this work, several methodological improvements were explored to enhance SWI quality and contrast, and specifically its ability for thalamic imaging.

METHODS

High-resolution SWI was performed at 7T in healthy participants, and the following techniques were applied: (a) monitoring and retrospective correction of head motion and B₀ perturbations using integrated MR navigators, (b) segmentation and removal of venous vessels on the SWI data using vessel enhancement filtering, and (c) contrast enhancement by tuning the parameters of the SWI phase-magnitude combination. The resulting improvements were evaluated with quantitative metrics of image quality, and by comparison to anatomo-histological thalamic atlases.

RESULTS

Even with sub-millimeter motion and natural breathing, motion and field correction produced clear improvements in both magnitude and phase data quality (76% and 41%, respectively). The improvements were stronger in cases of larger motion/field deviations, mitigating the dependence of image quality on subject performance. Optimizing the SWI phase-magnitude combination yielded substantial improvements in image contrast, particularly in the thalamus, well beyond previously reported SWI results. The atlas comparisons provided compelling evidence of anatomical correspondence between SWI features and several thalamic nuclei, for example, the ventral intermediate nucleus. Vein detection performed favorably inside the thalamus, and vein removal further improved visualization.

CONCLUSION

Altogether, the proposed developments substantially improve high-resolution SWI, particularly for thalamic nuclei imaging.

Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit

The implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.

Cardio-respiratory signal extraction from video camera data for continuous non-contact vital sign monitoring using deep learning

Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO2), by measuring subtle color changes on the skin surface using a video camera. For patients in a hospital ward, the main challenges in the development of continuous and robust non-contact monitoring techniques are the identification of time periods and the segmentation of skin regions of interest (ROIs) from which vital signs can be estimated. We propose a deep learning framework to tackle these challenges.

APPROACH

This paper presents two convolutional neural network (CNN) models. The first network was designed for detecting the presence of a patient and segmenting the patient's skin area. The second network combined the output from the first network with optical flow for identifying time periods of clinical intervention so that these periods can be excluded from the estimation of vital signs. Both networks were trained using video recordings from a clinical study involving 15 pre-term infants conducted in the high dependency area of the neonatal intensive care unit (NICU) of the John Radcliffe Hospital in Oxford, UK.

MAIN RESULTS

Our proposed methods achieved an accuracy of 98.8% for patient detection, a mean intersection-over-union (IOU) score of 88.6% for skin segmentation and an accuracy of 94.5% for clinical intervention detection using two-fold cross validation. Our deep learning models produced accurate results and were robust to different skin tones, changes in light conditions, pose variations and different clinical interventions by medical staff and family visitors.

SIGNIFICANCE

Our approach allows cardio-respiratory signals to be continuously derived from the patient's skin during which the patient is present and no clinical intervention is undertaken.

Alpha/beta power decreases track the fidelity of stimulus-specific information

Massed synchronised neuronal firing is detrimental to information processing. When networks of task-irrelevant neurons fire in unison, they mask the signal generated by task-critical neurons. On a macroscopic level, such synchronisation can contribute to alpha/beta (8-30 Hz) oscillations. Reducing the amplitude of these oscillations, therefore, may enhance information processing. Here, we test this hypothesis. Twenty-one participants completed an associative memory task while undergoing simultaneous EEG-fMRI recordings. Using representational similarity analysis, we quantified the amount of stimulus-specific information represented within the BOLD signal on every trial. When correlating this metric with concurrently-recorded alpha/beta power, we found a significant negative correlation which indicated that as post-stimulus alpha/beta power decreased, stimulus-specific information increased. Critically, we found this effect in three unique tasks: visual perception, auditory perception, and visual memory retrieval, indicating that this phenomenon transcends both stimulus modality and cognitive task. These results indicate that alpha/beta power decreases parametrically track the fidelity of both externally-presented and internally-generated stimulus-specific information represented within the cortex.

Radiographic estimation of the growth rate of initially underdiagnosed ameloblastomas

BACKGROUND

To evaluate the specific growth rate (SGR) of ameloblastoma.

MATERIAL AND METHODS

cases of ameloblastoma initially underdiagnosed (e.g. cases overlooked or diagnosed as reactive lesions) which had adequate radiographic documentation to evaluate their progression were retrospectively selected. Two panoramic radiographs were analyzed to determine the specific growth rate (SGR) of each tumor, defined as the logarithm of the ratio of final tumor area (when the diagnosis of ameloblastoma was made) to the initial tumor area (when the lesion was underdiagnosed), divided by the time interval between the radiographic images. The tumor area was measured using the software ImageJ.

RESULTS

Twelve patients with mandibular ameloblastomas were selected, including 5 males and 7 females, with a mean age of 24.9 years (range: 14-61 years). In four cases, the lesion was associated with the crown of an impacted third molar. In three cases, it was initially diagnosed as a periapical lesion. Three cases were extrafollicular and were not noticed in the initial radiographs. Two cases were initially diagnosed as ameloblastoma, but the surgery was delayed for personal reasons. The mean interval of time between the two radiographic images was 4.3 years (range: 0.4-9 years). Based on our analysis, ameloblastoma grows in average 40.4% per year (range: 14.9-88.7%).

CONCLUSIONS

Ameloblastoma is a progressively growing tumor, but its growth rate seems to be smaller than initially reported in the literature. Better understanding the radiographic progression of ameloblastoma might improve its early diagnosis, management, and prognosis.

Contribution of isotopic research techniques to characterize high-mountain-Mediterranean karst aquifers: The Port del Comte (Eastern Pyrenees) aquifer

Water resources in high mountain karst aquifers are usually characterized by high rainfall, recharge and discharge that lead to the sustainability of the downstream ecosystems. Nevertheless, these hydrological systems are vulnerable to the global change impact. The mean transit time (MTT) is a key parameter to describe the behavior of these hydrologic systems and also to assess their vulnerability. This work is focused on estimating MTT by using environmental tracers in the framework of high-mountain karst systems with a very thick unsaturated zone (USZ). To this end, it is adapted to alpine zones a methodology that combines a semi-distributed rainfall-runoff model to estimate recharge time series, and a lumped-parameter model to obtain ΜΤΤ. The methodology has been applied to the Port del Comte Massif (PCM) hydrological system (Southeastern Pyrenees, NE Spain), a karst aquifer system with an overlying 1000 m thick USZ. Six catchment areas corresponding to most important springs of the system are considered. The obtained results show that hydrologically the behavior of the system can be described by an exponential flow model (EM), with MTT ranging between 1.9 and 2.9 years. These ΜΤΤ values are shorter than those obtained by considering a constant recharge rate along time, which is the easiest and most applied aquifer recharge hypothesis when estimating ΜΤΤ through lumped-parameter models.

Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T

The ventro-intermediate nucleus (Vim), as part of the motor thalamic nuclei, is a commonly used target in functional stereotactic neurosurgery for treatment of drug-resistant tremor. As it cannot be directly visualized on routinely used magnetic resonance imaging (MRI), its clinical targeting is performed using indirect methods. Recent literature suggests that the Vim can be directly visualized on susceptibility-weighted imaging (SWI) acquired at 7 T. Our work aims to assess the distinguishable Vim on 7 T SWI in both healthy-population and patients and, using it as a reference, to compare it with: (1) The clinical targeting, (2) The automated parcellation of thalamic subparts based on 3 T diffusion MRI (dMRI), and (3) The multi-atlas segmentation techniques. In 95.2% of the data, the manual outline was adjacent to the inferior lateral border of the dMRI-based motor-nuclei group, while in 77.8% of the involved cases, its ventral part enclosed the Guiot points. Moreover, the late MRI signature in the patients was always observed in the anterior part of the manual delineation and it overlapped with the multi-atlas outline. Overall, our study provides new insight on Vim discrimination through MRI and imply novel strategies for its automated segmentation, thereby opening new perspectives for standardizing the clinical targeting.

Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI

Simultaneous EEG‐fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner's static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed. A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post‐processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moiré Phase Tracker (MPT) markers and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines. All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post‐processing using a multichannel, recursive least squares (M‐RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M‐RLS currently provides the best compromise between EEG data quality and practicalities of motion detection.

Mapping and characterization of positive and negative BOLD responses to visual stimulation in multiple brain regions at 7T

External stimuli and tasks often elicit negative BOLD responses in various brain regions, and growing experimental evidence supports that these phenomena are functionally meaningful. In this work, the high sensitivity available at 7T was explored to map and characterize both positive (PBRs) and negative BOLD responses (NBRs) to visual checkerboard stimulation, occurring in various brain regions within and beyond the visual cortex. Recently-proposed accelerated fMRI techniques were employed for data acquisition, and procedures for exclusion of large draining vein contributions, together with ICA-assisted denoising, were included in the analysis to improve response estimation. Besides the visual cortex, significant PBRs were found in the lateral geniculate nucleus and superior colliculus, as well as the pre-central sulcus; in these regions, response durations increased monotonically with stimulus duration, in tight covariation with the visual PBR duration. Significant NBRs were found in the visual cortex, auditory cortex, default-mode network (DMN) and superior parietal lobule; NBR durations also tended to increase with stimulus duration, but were significantly less sustained than the visual PBR, especially for the DMN and superior parietal lobule. Responses in visual and auditory cortex were further studied for checkerboard contrast dependence, and their amplitudes were found to increase monotonically with contrast, linearly correlated with the visual PBR amplitude. Overall, these findings suggest the presence of dynamic neuronal interactions across multiple brain regions, sensitive to stimulus intensity and duration, and demonstrate the richness of information obtainable when jointly mapping positive and negative BOLD responses at a whole-brain scale, with ultra-high field fMRI.

Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7 T

PURPOSE

Physiological noise often dominates the blood-oxygen level-dependent (BOLD) signal fluctuations in high-field functional MRI (fMRI) data. Therefore, to optimize fMRI protocols, it becomes crucial to investigate how physiological signal fluctuations impact various acquisition and reconstruction schemes at different acquisition speeds. In particular, further differences can arise between 2D and 3D fMRI acquisitions due to different encoding strategies, thereby impacting fMRI sensitivity in potentially significant ways.

METHODS

The amount of physiological noise to be removed from the BOLD fMRI signal acquired at 7 T was quantified for different sampling rates (repetition time from 3300 to 350 ms, acceleration 1 to 8) and techniques dedicated to fast fMRI (simultaneous multislice echo planar imaging [EPI] and 3D EPI). Resting state fMRI (rsfMRI) performances were evaluated using temporal signal-to-noise ratio (tSNR) and network characterization based on seed correlation and independent component analysis.

RESULTS

Overall, acceleration enhanced tSNR and rsfMRI metrics. 3D EPI benefited the most from physiological noise removal at long repetition times. Differences between 2D and 3D encoding strategies disappeared at high acceleration factors (6- to 8-fold).

CONCLUSION

After physiological noise correction, 2D- and 3D-accelerated sequences provide similar performances at high fields, both in terms of tSNR and resting state network identification and characterization. Magn Reson Med 78:888-896, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Presurgical brain mapping in epilepsy using simultaneous EEG and functional MRI at ultra-high field: feasibility and first results

OBJECTIVES

The aim of this study was to demonstrate that eloquent cortex and epileptic-related hemodynamic changes can be safely and reliably detected using simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) recordings at ultra-high field (UHF) for clinical evaluation of patients with epilepsy.

MATERIALS AND METHODS

Simultaneous EEG-fMRI was acquired at 7 T using an optimized setup in nine patients with lesional epilepsy. According to the localization of the lesion, mapping of eloquent cortex (language and motor) was also performed in two patients.

RESULTS

Despite strong artifacts, efficient correction of intra-MRI EEG could be achieved with optimized artifact removal algorithms, allowing robust identification of interictal epileptiform discharges. Noise-sensitive topography-related analyses and electrical source localization were also performed successfully. Localization of epilepsy-related hemodynamic changes compatible with the lesion were detected in three patients and concordant with findings obtained at 3 T. Local loss of signal in specific regions, essentially due to B 1 inhomogeneities were found to depend on the geometric arrangement of EEG leads over the cap.

CONCLUSION

These results demonstrate that presurgical mapping of epileptic networks and eloquent cortex is both safe and feasible at UHF, with the benefits of greater spatial resolution and higher blood-oxygenation-level-dependent sensitivity compared with the more traditional field strength of 3 T.

Fourier modeling of the BOLD response to a breath-hold task: Optimization and reproducibility

Cerebrovascular reactivity (CVR) reflects the capacity of blood vessels to adjust their caliber in order to maintain a steady supply of brain perfusion, and it may provide a sensitive disease biomarker. Measurement of the blood oxygen level dependent (BOLD) response to a hypercapnia-inducing breath-hold (BH) task has been frequently used to map CVR noninvasively using functional magnetic resonance imaging (fMRI). However, the best modeling approach for the accurate quantification of CVR maps remains an open issue. Here, we compare and optimize Fourier models of the BOLD response to a BH task with a preparatory inspiration, and assess the test-retest reproducibility of the associated CVR measurements, in a group of 10 healthy volunteers studied over two fMRI sessions. Linear combinations of sine-cosine pairs at the BH task frequency and its successive harmonics were added sequentially in a nested models approach, and were compared in terms of the adjusted coefficient of determination and corresponding variance explained (VE) of the BOLD signal, as well as the number of voxels exhibiting significant BOLD responses, the estimated CVR values, and their test-retest reproducibility. The brain average VE increased significantly with the Fourier model order, up to the 3rd order. However, the number of responsive voxels increased significantly only up to the 2nd order, and started to decrease from the 3rd order onwards. Moreover, no significant relative underestimation of CVR values was observed beyond the 2nd order. Hence, the 2nd order model was concluded to be the optimal choice for the studied paradigm. This model also yielded the best test-retest reproducibility results, with intra-subject coefficients of variation of 12 and 16% and an intra-class correlation coefficient of 0.74. In conclusion, our results indicate that a Fourier series set consisting of a sine-cosine pair at the BH task frequency and its two harmonics is a suitable model for BOLD-fMRI CVR measurements based on a BH task with preparatory inspiration, yielding robust estimates of this important physiological parameter.

Non-contact measurement of oxygen saturation with an RGB camera

A novel method (Sophia) is presented to track oxygen saturation changes in a controlled environment using an RGB camera placed approximately 1.5 m away from the subject. The method is evaluated on five healthy volunteers (Fitzpatrick skin phenotypes II, III, and IV) whose oxygen saturations were varied between 80% and 100% in a purpose-built chamber over 40 minutes each. The method carefully selects regions of interest (ROI) in the camera image by calculating signal-to-noise ratios for each ROI. This allows it to track changes in oxygen saturation accurately with respect to a conventional pulse oximeter (median coefficient of determination, 0.85).

Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion

The enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7 T, from healthy volunteers undergoing a reversing-checkerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.

Continuous non-contact vital sign monitoring in neonatal intensive care unit

Current technologies to allow continuous monitoring of vital signs in pre-term infants in the hospital require adhesive electrodes or sensors to be in direct contact with the patient. These can cause stress, pain, and also damage the fragile skin of the infants. It has been established previously that the colour and volume changes in superficial blood vessels during the cardiac cycle can be measured using a digital video camera and ambient light, making it possible to obtain estimates of heart rate or breathing rate. Most of the papers in the literature on non-contact vital sign monitoring report results on adult healthy human volunteers in controlled environments for short periods of time. The authors' current clinical study involves the continuous monitoring of pre-term infants, for at least four consecutive days each, in the high-dependency care area of the Neonatal Intensive Care Unit (NICU) at the John Radcliffe Hospital in Oxford. The authors have further developed their video-based, non-contact monitoring methods to obtain continuous estimates of heart rate, respiratory rate and oxygen saturation for infants nursed in incubators. In this Letter, it is shown that continuous estimates of these three parameters can be computed with an accuracy which is clinically useful. During stable sections with minimal infant motion, the mean absolute error between the camera-derived estimates of heart rate and the reference value derived from the ECG is similar to the mean absolute error between the ECG-derived value and the heart rate value from a pulse oximeter. Continuous non-contact vital sign monitoring in the NICU using ambient light is feasible, and the authors have shown that clinically important events such as a bradycardia accompanied by a major desaturation can be identified with their algorithms for processing the video signal.

EEG-fMRI integration for the study of human brain function

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have proved to be extremely valuable tools for the non-invasive study of human brain function. Moreover, due to a notable degree of complementarity between the two modalities, the combination of EEG and fMRI data has been actively sought in the last two decades. Although initially focused on epilepsy, EEG-fMRI applications were rapidly extended to the study of healthy brain function, yielding new insights into its underlying mechanisms and pathways. Nevertheless, EEG and fMRI have markedly different spatial and temporal resolutions, and probe neuronal activity through distinct biophysical processes, many aspects of which are still poorly understood. The remarkable conceptual and methodological challenges associated with EEG-fMRI integration have motivated the development of a wide range of analysis approaches over the years, each relying on more or less restrictive assumptions, and aiming to shed further light on the mechanisms of brain function along with those of the EEG-fMRI coupling itself. Here, we present a review of the most relevant EEG-fMRI integration approaches yet proposed for the study of brain function, supported by a general overview of our current understanding of the biophysical mechanisms coupling the signals obtained from the two modalities.

Central corneal thickness and anterior chamber depth measurement by Sirius(®) Scheimpflug tomography and ultrasound

Background

The purpose of this study was to compare the accuracy of the new Sirius^® Scheimpflug anterior segment examination device for measurement of central corneal thickness (CCT) and anterior chamber depth (ACD) with that of CCT measurements obtained by ultrasound pachymetry and ACD measurements obtained by ultrasound biometry, respectively.

Methods

CCT and ACD was measured in 50 right eyes from 50 healthy subjects using a Sirius Scheimpflug camera, SP100 ultrasound pachymetry, and US800 ultrasound biometry.

Results

CCT measured with the Sirius was 546 ± 39 μm and 541 ± 35 μm with SP100 ultrasound pachymetry (P = 0.003). The difference was statistically significant (mean difference 4.68 ± 10.5 μm; limits of agreement −15.8 to 25.20 μm). ACD measured with the Sirius was 2.96 ± 0.3 mm compared with 3.36 ± 0.29 mm using US800 ultrasound biometry (P < 0.001). The difference was statistically significant (mean difference −0.40 ± 0.16 mm; limits of agreement −0.72 to 0.07 mm). When the ACD values obtained using ultrasound biometry were corrected according to the values for CCT measured by ultrasound, the agreement increased significantly between both technologies for ACD measurements (mean difference 0.15 ± 0.16 mm; limits of agreement −0.16 to 0.45 mm).

Conclusion

CCT and ACD measured by Sirius and ultrasound methods showing good agreement between repeated measurements obtained in the same subjects (repeatability) with either instrument. However, CCT and ACD values, even after correcting ultrasound ACD by subtracting the CCT value obtained with either technology should not be used interchangeably.

Signal fluctuations in fMRI data acquired with 2D-EPI and 3D-EPI at 7 Tesla

Segmented three-dimensional echo planar imaging (3D-EPI) provides higher image signal-to-noise ratio (SNR) than standard single-shot two-dimensional echo planar imaging (2D-EPI), but is more sensitive to physiological noise. The aim of this study was to compare physiological noise removal efficiency in single-shot 2D-EPI and segmented 3D-EPI acquired at 7 Tesla. Two approaches were investigated based either on physiological regressors (PR) derived from cardiac and respiratory phases, or on principal component analysis (PCA) using additional resting-state data. Results show that, prior to physiological noise removal, 2D-EPI data had higher temporal SNR (tSNR), while spatial SNR was higher in 3D-EPI. Blood oxygen level dependent (BOLD) sensitivity was similar for both methods. The PR-based approach allowed characterization of relative contributions from different noise sources, confirming significant increases in physiological noise from 2D to 3D prior to correction. Both physiological noise removal approaches produced significant increases in tSNR and BOLD sensitivity, and these increases were larger for 3D-EPI, resulting in higher BOLD sensitivity in the 3D-EPI than in the 2D-EPI data. The PCA-based approach was the most effective correction method, yielding higher tSNR values for 3D-EPI than for 2D-EPI postcorrection.

Clinical evaluation of the IOPen® in a glaucomatous population

The aim of this study was to evaluate the level of agreement of measurements of intraocular pressure (IOP) taken by a rebound tonometer (IOPen®), in comparison to a reference Goldmann applanation tonometer (GAT) in a glaucomatous population. Both eyes from 60 patients were assessed with the two tonometers, the induction tonometry was performed first by an experienced optometrist, and the GAT by an ophthalmologist. In this study, statistically significant differences were found when comparing the IOPen® tonometer with the GAT tonometer (p < 0.001), mean differences were -4.81 ± 4.31 and -4.76 ± 5.76 mmHg (mean ± S.D.) for the right eye and left eye respectively These values represent an underestimation in the present population by the IOPen® when compared with the GAT. Frequency distribution of differences demonstrated that in more than 71.6% of the measurements the IOP readings differed by more than 3 mmHg between the two tonometers. These results suggest that IOPen® should be used with great caution in the determination of IOP.