Discovering individual fingerprints in resting-state functional connectivity using deep neural networks.

Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the "fingerprint of FC" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.

Use of functional magnetic resonance imaging to identify cortical loci for lower limb movements and their efficacy for individuals after stroke.

BACKGROUND: Identification of cortical loci for lower limb movements for stroke rehabilitation is crucial for better rehabilitation outcomes via noninvasive brain stimulation by targeting the fine-grained cortical loci of the movements. However, identification of the cortical loci for lower limb movements using functional MRI (fMRI) is challenging due to head motion and difficulty in isolating different types of movement. Therefore, we developed a custom-made MR-compatible footplate and leg cushion to identify the cortical loci for lower limb movements and conducted multivariate analysis on the fMRI data. We evaluated the validity of the identified loci using both fMRI and behavioral data, obtained from healthy participants as well as individuals after stroke. METHODS: We recruited 33 healthy participants who performed four different lower limb movements (ankle dorsiflexion, ankle rotation, knee extension, and toe flexion) using our custom-built equipment while fMRI data were acquired. A subgroup of these participants (Dataset 1; n = 21) was used to identify the cortical loci associated with each lower limb movement in the paracentral lobule (PCL) using multivoxel pattern analysis and representational similarity analysis. The identified cortical loci were then evaluated using the remaining healthy participants (Dataset 2; n = 11), for whom the laterality index (LI) was calculated for each lower limb movement using the cortical loci identified for the left and right lower limbs. In addition, we acquired a dataset from 15 individuals with chronic stroke for regression analysis using the LI and the Fugl-Meyer Assessment (FMA) scale. RESULTS: The cortical loci associated with the lower limb movements were hierarchically organized in the medial wall of the PCL following the cortical homunculus. The LI was clearer using the identified cortical loci than using the PCL. The healthy participants (mean ± standard deviation: 0.12 ± 0.30; range: - 0.63 to 0.91) exhibited a higher contralateral LI than the individuals after stroke (0.07 ± 0.47; - 0.83 to 0.97). The corresponding LI scores for individuals after stroke showed a significant positive correlation with the FMA scale for paretic side movement in ankle dorsiflexion (R2 = 0.33, p = 0.025) and toe flexion (R2 = 0.37, p = 0.016). CONCLUSIONS: The cortical loci associated with lower limb movements in the PCL identified in healthy participants were validated using independent groups of healthy participants and individuals after stroke. Our findings suggest that these cortical loci may be beneficial for the neurorehabilitation of lower limb movement in individuals after stroke, such as in developing effective rehabilitation interventions guided by the LI scores obtained for neuronal activations calculated from the identified cortical loci across the paretic and non-paretic sides of the brain.

Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans.

We investigated neural representations for visual perception of 10 handwritten digits and six visual objects from a convolutional neural network (CNN) and humans using functional magnetic resonance imaging (fMRI). Once our CNN model was fine-tuned using a pre-trained VGG16 model to recognize the visual stimuli from the digit and object categories, representational similarity analysis (RSA) was conducted using neural activations from fMRI and feature representations from the CNN model across all 16 classes. The encoded neural representation of the CNN model exhibited the hierarchical topography mapping of the human visual system. The feature representations in the lower convolutional (Conv) layers showed greater similarity with the neural representations in the early visual areas and parietal cortices, including the posterior cingulate cortex. The feature representations in the higher Conv layers were encoded in the higher-order visual areas, including the ventral/medial/dorsal stream and middle temporal complex. The neural representations in the classification layers were observed mainly in the ventral stream visual cortex (including the inferior temporal cortex), superior parietal cortex, and prefrontal cortex. There was a surprising similarity between the neural representations from the CNN model and the neural representations for human visual perception in the context of the perception of digits versus objects, particularly in the primary visual and associated areas. This study also illustrates the uniqueness of human visual perception. Unlike the CNN model, the neural representation of digits and objects for humans is more widely distributed across the whole brain, including the frontal and temporal areas.

Reliability of self-reported dispositional mindfulness scales and their association with working memory performance and functional connectivity.

We systematically investigated the link between trait mindfulness scores and functional connectivity (FC) features or behavioral data, to emphasize the importance of the reliability of self-report mindfulness scores. Sixty healthy young male participants underwent two functional MRI runs with three mindfulness or mind-wandering task blocks with an N-back task (NBT) block. The data from 49 participants (age: 23.3 ± 2.8) for whom two sets of the self-reported Mindfulness Attention Awareness Scale (MAAS) and NBT performance were available were analyzed. We divided participants into two groups based on the consistency level of their MAAS scores (i.e., a "consistent" and an "inconsistent" group). Then, the association between the MAAS scores and FC features or NBT performance was investigated using linear regression analysis with p-value correction and bootstrapping. Meaningful associations (a) between MAAS and NBT accuracy (slope = 0.41, CI = [0.10, 0.73], corrected p < 0.05), (b) between MAAS and the FC edges in the frontoparietal network, and (c) between the FC edges and NBT performance were only observed in the consistent group (n = 26). Our findings demonstrate the importance of appropriate screening mechanisms for self-report-based dispositional mindfulness scores when trait mindfulness scores are combined with neuronal features and behavioral data.

Simulation Analysis of Flow Rate Variability During Microinfusions: The Effect of Vertical Displacement and Multidrug Infusion in Conventional Infusion Pumps Versus New Cylinder-Type Infusion Pumps.

BACKGROUND: Medication dosing errors can occur during microinfusions when there is vertical pump displacement or multidrug infusion through a single intravenous path. We compared flow rate variability between new-generation cylinder-type infusion pumps and conventional infusion pumps under simulated conditions. METHODS: We evaluated the flow rates during microinfusions using different infusion pumps (syringe pump with 10/30/50-mL syringes, peristaltic pump, and cylinder pump). Two visible dyes were used as model drugs. The study samples were quantified using spectrophotometry. For vertical displacement, the infusion pumps were moved up and down by 60 cm during microinfusions at 0.5 mL·h-1 and 2 mL·h-1. In the multi-infusion study, the second drug flow was added through 4 linearly connected stopcocks either upstream or downstream of the first drug. We compared the total error dose between the cylinder pump and the syringe pump with a Mann-Whitney U test and additionally estimated the effects of the infusion pumps on total error doses by linear regression analysis. RESULTS: There were repetitive patterns of temporary flow increases when the pump was displaced upward and flow decreases when the pump was displaced downward in all settings. However, the amount of flow irregularities was more pronounced at the lower infusion rate and in the syringe-type pump using larger volume syringes. The total error dose increased in the syringe pump loaded with a 50-mL syringe compared to that of the new cylinder pump (regression coefficient [ß] = 4.66 [95% confidence interval {CI}, 1.60-7.72]; P = .008). The initiation and cessation of a new drug during multidrug microinfusion in the same intravenous path affected the lower rate first drug leading to a transient flow rate increase and decrease, respectively. The change in flow rate was observed regardless of the port selected for addition of the second drug, and the total error dose of the first drug did not significantly vary when an upstream or a downstream port was selected. CONCLUSIONS: In the microinfusion settings, attention must be paid to the use of the syringe pump loaded with large-volume syringes. The novel cylinder pump could be considered as a practical alternative to syringe pumps with small syringes given its flow stability without the need for frequent drug replacement.

Risk factors for femoral-to-radial artery pressure gradient after weaning from cardiopulmonary bypass: a historical cohort study.

PURPOSE: After weaning from cardiopulmonary bypass (CPB), the radial artery pressure is frequently lower than the central pressure as reflected by femoral pressure. This discrepancy may cause improper blood pressure management. In this study, we aimed to evaluate the risk factors related to developing a significant postbypass femoral-to-radial pressure gradient, including the incidence of complications related to femoral pressure monitoring. METHODS: From January 2017 to May 2021, we studied consecutive adult cardiovascular surgical patients undergoing CPB in a historical cohort study. Patients were divided into two groups according to developing a significant femoral-to-radial pressure gradient, which was defined as a difference of ≥ 25 mm Hg for systolic pressure or ≥ 10 mm Hg for mean pressure, lasting ≥ 5 minutes for 30 minutes after CPB weaning. Factors associated with a significant pressure gradient and femoral pressure monitoring-related complications were analyzed. RESULTS: Among 2,019 patients, 677 (34%) showed a significant postbypass femoral-to-radial pressure gradient. Multivariable logistic regression analysis revealed the following factors related to the pressure gradient development: age (adjusted odds ratio [aOR] for an increase in 10 years, 1.09; 95% confidence interval [CI], 1.04 to 1.09; P < 0.001), body surface area (BSA) (aOR for an increase in 1 m2, 0.12; 95% CI, 0.07 to 0.21; P < 0.001), aortic cross-clamping time (aOR for an increase in 30 minutes, 1.05; 95% CI, 1.03 to 1.08; P < 0.001), and intraoperative epinephrine use (aOR, 1.55; 95% CI, 1.23 to 1.95; P < 0.001). The femoral pressure monitoring-related complications were observed in 11/2,019 (0.5%) patients. CONCLUSION: Our study showed that old age, smaller BSA, prolonged aortic cross-clamping time, and intraoperative epinephrine use were associated with developing a significant postbypass femoral-to-radial pressure gradient in cardiovascular surgery. Considering monitoring-related complications occurred very infrequently, it might be helpful to monitor both radial and femoral pressure simultaneously in patients with these risk factors for appropriate blood pressure management. Nevertheless, further studies are needed to confirm our findings because our results are limited by a retrospective design and residual confounding factors.

RéSUMé: OBJECTIF: Après le sevrage de la circulation extracorporelle (CEC), la pression dans l'artère radiale est souvent inférieure à la pression centrale, comme en témoigne la pression fémorale. Cette divergence peut provoquer une gestion inadaptée de la tension artérielle. Dans cette étude, nous avons cherché à évaluer les facteurs de risque liés au développement d'un gradient de pression significatif fémoro-radial post CEC, y compris l'incidence des complications liées à la surveillance de la pression fémorale. MéTHODES: Nous avons étudié consécutivement, de janvier 2017 à mai 2021, des patients adultes de chirurgie cardiovasculaire subissant une CEC dans une étude de cohorte historique. Les patients ont été séparés en deux groupes en fonction de la survenue d'un gradient de pression fémoro-radial significatif qui était défini ainsi : différence de ≥ 25 mmHg pour la pression systolique ou ≥ 10 mmHg pour la pression moyenne, durant ≥ 5 minutes pendant les 30 minutes suivant le sevrage de la CEC. Les facteurs associés à un gradient de pression significatif et aux complications liées à la surveillance de la pression fémorale ont été analysés. RéSULTATS: Parmi les 2 019 patients, 677 (34 %) ont présenté un gradient de pression fémoro-radial significatif post CEC. Une analyse par régression logistique multifactorielle a révélé que les facteurs suivants étaient liés à la survenue du gradient de pression : l'âge (rapport de cotes ajusté [aOR] pour une augmentation en dix ans, 1,09; intervalle de confiance [IC] à 95 % : 1,04 à 1,09; P < 0,001), la surface corporelle (SC) (aOR pour une augmentation dans 1 m2, 0,12; IC à 95 %, 0,07 à 0,21; P < 0,001), la durée du clampage aortique (aOR pour une augmentation en 30 minutes, 1,05; IC à 95 %, 1,03 à 1,08; P < 0,001) et l'utilisation peropératoire d'épinéphrine (aOR, 1,55; IC à 95 %, 1,23 à 1,95; P < 0,001). Des complications liées à la surveillance de la pression fémorale ont été observées chez 11 patients sur 2019 (0,5 %). CONCLUSION: Notre étude a montré qu'un âge avancé, une petite SC, une durée prolongée de clampage aortique et l'utilisation peropératoire d'épinéphrine étaient associés à la survenue d'un gradient de pression fémoro-radial significatif en chirurgie cardiovasculaire. Considérant que les complications liées à la surveillance ont été très rares, il pourrait être utile de surveiller simultanément la pression radiale et la pression fémorale chez les patients ayant ces facteurs de risque pour une gestion appropriée de la pression artérielle. Néanmoins, d'autres études sont nécessaires pour confirmer nos constatations, car ces résultats sont limités par le plan rétrospectif de l'étude et des facteurs confondants résiduels.

3D Point Cloud Acquisition and Correction in Radioactive and Underwater Environments Using Industrial 3D Scanners.

This study proposes a method to acquire an accurate 3D point cloud in radioactive and underwater environments using industrial 3D scanners. Applications of robotic systems at nuclear facility dismantling require 3D imaging equipment for localization of target structures in radioactive and underwater environments. The use of industrial 3D scanners may be a better option than developing prototypes for researchers with basic knowledge. However, such industrial 3D scanners are designed to operate in normal environments and cannot be used in radioactive and underwater environments. Modifications to environmental obstacles also suffer from hidden technical details of industrial 3D scanners. This study shows how 3D imaging equipment based on the industrial 3D scanner satisfies the requirements of the remote dismantling system, using a robotic system despite insufficient environmental resistance and hidden technical details of industrial 3D scanners. A housing unit is designed for waterproofing and radiation protection using windows, mirrors and shielding. Shielding protects the industrial 3D scanner from radiation damage. Mirrors reflect the light required for 3D scanning because shielding blocks the light. Windows in the waterproof housing also transmit the light required for 3D scanning with the industrial 3D scanner. The basic shielding thickness calculation method through the experimental method is described, including the analysis of the experimental results. The method for refraction correction through refraction modeling, measurement experiments and parameter studies are described. The developed 3D imaging equipment successfully satisfies the requirements of the remote dismantling system: waterproof, radiation resistance of 1 kGy and positional accuracy within 1 mm. The proposed method is expected to provide researchers with an easy approach to 3D scanning in radioactive and underwater environments.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Mixed-effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies.

We report that regions-of-interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed-effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real-world dataset for the neuronal response to nicotine use was acquired using a custom-made MRI-compatible apparatus for the smoking of electronic cigarettes (e-cigarettes). Nineteen participants smoked e-cigarettes in an MRI scanner using the apparatus with two experimental conditions: e-cigarettes with nicotine (ECIG) and sham e-cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ2 -test of the MEMA but not from the t-test, and the corresponding activations were significantly associated with the similarity scores (r = -.52, p = .041, confidence interval [CI] = [-0.78, -0.17]) and the urge-to-smoke scores (r = .73, p <.001, CI = [0.52, 0.88]). From the contrast between the two conditions (i.e., ECIG > SCIG), the right orbitofrontal cortex was identified from the χ2 -tests, and the corresponding neuronal activations showed a statistically meaningful association with similarity (r = -.58, p = .01, CI = [-0.84, -0.17]) and the urge to smoke (r = .34, p = .15, CI = [0.09, 0.56]). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was further evaluated using the fMRI and behavioral data acquired from the working memory and gambling tasks available from the Human Connectome Project.

Imaging localized fast optical signals of neural activation with optical coherence tomography in awake mice.

We report optical coherence tomography (OCT) imaging of localized fast optical signals (FOSs) arising from whisker stimulation in awake mice. The activated voxels were identified by fitting the OCT intensity signal time course with a response function over a time scale of a few hundred milliseconds after the whisker stimulation. The significantly activated voxels were shown to be localized to the expected brain region for whisker stimulation. The ability to detect functional stimulus-evoked, depth-resolved FOS with intrinsic contrast from the cortex provides a new tool for neural activity studies.

Prognosis of Myocardial Injury After Non-Cardiac Surgery in Adults Aged Younger Than 45 Years.

BACKGROUND: This study compared myocardial injury after non-cardiac surgery (MINS) and mortalities between patients under and over the age of 45 years.Methods and Results:From January 2010 and June 2019, patients with cardiac troponin measurement within 30 days after non-cardiac surgery were enrolled and divided into groups according to age: >45 (≥45 years) and <45 (<45 years). Further analyses were conducted only in patients who were diagnosed with MINS. The outcomes were MINS and 30-day mortality. Of the 35,223 patients, 31,161 (88.5%) patients were in the >45-year group and 4,062 (11.5%) were in the <45-year group. After adjustment with inverse probability of weighting, the <45-years group showed a lower incidence of MINS and cardiovascular mortality (16.6% vs. 11.7%; odds ratio, 0.77; 95% confidence interval [CI], 0.69-0.84; P<0.001 and 0.4% vs. 0.2%; hazard ratio [HR], 0.41; 95% CI, 0.19-0.88; P=0.02, respectively). In a comparison of only the <45-years group, MINS was associated with increased 30-day mortality (0.7% vs. 10.3%; HR, 10.48; 95% CI, 6.18-17.78; P<0.001), but the mortalities of patients with MINS did not differ according to age. CONCLUSIONS: MINS has a comparable prognostic impact in patients aged under and over 45 years; therefore, future studies need to also consider patients aged <45 years regarding risk factors of MINS and screening of perioperative troponin elevation.

Evaluation of the factors related to difficult ultrasound-guided radial artery catheterization in small children: A prospective observational study.

BACKGROUND: Although ultrasound guidance has significantly improved the success rate of radial artery catheterization, the failure rate in children is still high. For the further improvement of success rate, we prospectively evaluated the factors that make ultrasound-guided radial artery catheterization difficult in children under two years old. METHODS: From October 2018 to September 2019, patients who required radial artery catheterization for surgery were enrolled. After collecting the anatomical characteristics of the radial artery using ultrasound at the puncture site, ultrasound-guided radial artery catheterization was performed by one experienced anaesthesiologist. The primary outcome was to identify the factors related to the first attempt failure. The factors associated with the total duration of the procedure until success were also evaluated. RESULTS: A total of 183 children were included in the analysis. A radial artery cross-sectional area of ≤1 mm2 (odds ratio [OR] = 5.26; 95% confidence interval [CI], 2.48-11.18; P < .0001) and the presence of an anomalous radial artery branch (OR = 3.37; 95% CI, 1.43-7.95; P = .005) were independent predictors of first-attempt failure during ultrasound-guided radial artery catheterization. The total procedure time was also negatively associated with the small cross-sectional area (P < .001). CONCLUSIONS: A cross-sectional area of ≤ 1 mm2 and the presence of an anomalous branch of radial artery significantly increased the difficulty of ultrasound-guided radial artery catheterization in children under two years old. In patients of these ages, pre-procedural ultrasound scanning to find an optimal site for catheterization may increase the first-attempt success rate although further studies are needed to verify our results. TRIAL REGISTRATION: Clinical Research Information Service (https://crits.nih.go.kr, October 6, 2018 [KCT0003239]; Principle investigator: Jong-Hwan Lee).

Simulation study on flow rate accuracy of infusion pumps in vibration conditions during emergency patient transport.

Infusion pumps are frequently used when transferring critically ill patients via patient transport cart, ambulance, or helicopter. However, the performance of various infusion pumps under these circumstances has not been explored. The aim of this study was to evaluate the flow rate accuracy of infusion pumps under various clinical vibration conditions. Experiments were conducted with four different types of pumps, including two conventional syringe pumps (Injectomat MC Agilia, Fresenius Kabi and TE-331, Terumo), one conventional peristaltic pump (Volumed µVP7000; Arcomed), and one new cylinder pump (H-100, Meinntech). The flow rate was measured using an infusion pump analyzer on a stable table (0 m/s2) for 1 h with 1 ml/h and 5 ml/h. Experiments were repeated in mild vibration (2 m/s2) (representing vibration of patients in a moving stretcher or ambulance), and in moderate vibration (6 m/s2) (representing vibration in helicopter transport). Any accidental bolus occurrence in extreme vibration situations (20 m/s2) was also analyzed. Simulated vibrations were reproduced by a custom-made vibration table. In the resting state without vibration and in mild vibration conditions, all pumps maintained good performance. However, in moderate vibration, flow rates in syringe pumps increased beyond their known error ranges, while flow rates in peristaltic pumps remained stable. In extreme vibration, accidental fluid bolus occurred in syringe pumps but not in peristaltic pumps. The newly developed cylinder pump maintained stable performance and was unaffected by external vibration environments.

fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.

Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area of the input sample and moves across the sample to provide a feature map for the subsequent layers. In our study, we hypothesized that a 3D-CNN model with down-sampling operations such as pooling and/or stride would have the ability to extract robust feature maps from the shifted and scaled neuronal activations in a single functional MRI (fMRI) volume for the classification of task information associated with that volume. Thus, the 3D-CNN model would be able to ameliorate the potential misalignment of neuronal activations and over-/under-activation in local brain regions caused by imperfections in spatial alignment algorithms, confounded by variability in blood-oxygenation-level-dependent (BOLD) responses across sessions and/or subjects. To this end, the fMRI volumes acquired from four sensorimotor tasks (left-hand clenching, right-hand clenching, auditory attention, and visual stimulation) were used as input for our 3D-CNN model to classify task information using a single fMRI volume. The classification performance of the 3D-CNN was systematically evaluated using fMRI volumes obtained from various minimal preprocessing scenarios applied to raw fMRI volumes that excluded spatial normalization to a template and those obtained from full preprocessing that included spatial normalization. Alternative classifier models such as the 1D fully connected DNN (1D-fcDNN) and support vector machine (SVM) were also used for comparison. The classification performance was also assessed for several k-fold cross-validation (CV) schemes, including leave-one-subject-out CV (LOOCV). Overall, the classification results of the 3D-CNN model were superior to that of the 1D-fcDNN and SVM models. When using the fully-processed fMRI volumes with LOOCV, the mean error rates (± the standard error of the mean) for the 3D-CNN, 1D-fcDNN, and SVM models were 2.1% (± 0.9), 3.1% (± 1.2), and 4.1% (± 1.5), respectively (p = 0.041 from a one-way ANOVA). The error rates for 3-fold CV were higher (2.4% ± 1.0, 4.2% ± 1.3, and 10.1% ± 2.0; p < 0.0003 from a one-way ANOVA). The mean error rates also increased considerably using the raw fMRI 3D volume data without preprocessing (26.2% for the 3D-CNN, 75.0% for the 1D-fcDNN, and 75.0% for the SVM). Furthermore, the ability of the pre-trained 3D-CNN model to handle shifted and scaled neuronal activations was demonstrated in an online scenario for five-class classification (i.e., four sensorimotor tasks and the resting state) using the real-time fMRI of three participants. The resulting classification accuracy was 78.5% (± 1.4), 26.7% (± 5.9), and 21.5% (± 3.1) for the 3D-CNN, 1D-fcDNN, and SVM models, respectively. The superior performance of the 3D-CNN compared to the 1D-fcDNN was verified by analyzing the resulting feature maps and convolution filters that handled the shifted and scaled neuronal activations and by utilizing an independent public dataset from the Human Connectome Project.

A naturalistic viewing paradigm using 360° panoramic video clips and real-time field-of-view changes with eye-gaze tracking.

The naturalistic viewing of a video clip enables participants to obtain more information from the clip compared to conventional viewing of a static image. Because changing the field-of-view (FoV) allows new visual information to be obtained, we were motivated to investigate whether naturalistic viewing with varying FoV based on active eye movement can enhance the viewing experience of natural stimuli, such as those found in a video clip with a 360° FoV in an MRI scanner. To this end, we developed a novel naturalistic viewing paradigm based on real-time eye-gaze tracking while participants were watching a 360° panoramic video during fMRI acquisition. The gaze position of the participants was recorded using an eye-tracking computer and then transmitted to a stimulus presentation computer via a TCP/IP connection. The identified gaze position was then used to alter the participants' FoV of the video clip in real-time, so the participants could change their FoV to fully explore the 360° video clip (referred to in this paper as active viewing). The gaze position of one participant while watching a video was used to change the FoV of the same video clip for a paired participant (referred to as yoked or passive viewing). Four 360° panoramic videos were used as stimuli, divided into categories based on the brightness level (i.e., bright vs. dark) and location (i.e., nature vs. city). Each of the subjects participated in the active viewing of one of the two nature videos and one of the two city videos and then engaged in the passive viewing of the other video in each category, followed by conventional viewing with a fixed FoV (referred to as fixed viewing) after each of the active or passive viewings. Forty-eight healthy volunteers participated in the study, and data from 42 of these participants were used in the analysis. Representational similarity analysis (RSA) was conducted in a multiple regression framework using representational dissimilarity matrix (RDM) codes to accommodate all of the information regarding neuronal activations from fMRI analysis and the participants' subjective ratings of their viewing experience with the four video clips and with the two contrasting viewing conditions (i.e., "active-fixed" and "passive-fixed"). It was found that the participants' naturalistic viewing experience of the video clips was substantially more immersive with active viewing than with passive and fixed viewing. The RSA using the RDM codes revealed the brain regions associated with the viewing experience, including eye movement and spatial navigation in the superior frontal area (of Brodmann's area 6) and the inferior/superior parietal areas, respectively. Brain regions potentially associated with cognitive and affective processing during the viewing of the video, such as the default-mode networks and insular/Rolandic operculum areas, were also identified. To the best of our knowledge, this is the first study that has used the participants' eye movements to interactively change their FoV for 360° panoramic video clips in real-time. Our method of utilizing the MRI environment can be further extended to other environments such as electroencephalography and behavioral research. It would also be feasible to apply our method to virtual reality and/or augmented reality systems to maximize user experience based on their eye movement.

Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning.

Competition and collaboration are strategies that can be used to optimize the outcomes of social interactions. Research into the neuronal substrates underlying these aspects of social behavior has been limited due to the difficulty in distinguishing complex activation via univariate analysis. Therefore, we employed multivoxel pattern analysis of functional magnetic resonance imaging to reveal the neuronal activations underlying competitive and collaborative processes when the collaborator/opponent used myopic/predictive reasoning. Twenty-four healthy subjects participated in 2 × 2 matrix-based sequential-move games. Searchlight-based multivoxel patterns were used as input for a support vector machine using nested cross-validation to distinguish game conditions, and identified voxels were validated via the regression of the behavioral data with bootstrapping. The left anterior insula (accuracy = 78.5%) was associated with competition, and middle frontal gyrus (75.1%) was associated with predictive reasoning. The inferior/superior parietal lobules (84.8%) and middle frontal gyrus (84.7%) were associated with competition, particularly in trials with a predictive opponent. The visual/motor areas were related to response time as a proxy for visual attention and task difficulty. Our results suggest that multivoxel patterns better represent the neuronal substrates underlying the social cognition of collaboration and competition intermixed with myopic and predictive reasoning than do univariate features.

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Effects of inspired oxygen concentration during emergence from general anaesthesia on postoperative lung impedance changes evaluated by electrical impedance tomography: a randomised controlled trial.

We evaluated the effects of three different inspired oxygen concentrations (40%, 80%, and 100%) at anaesthesia emergence on postoperative lung volumes as measured by global impedance of electrical impedance tomography (EIT). This is a randomised, controlled, and assessor-blinded study in single-centre from May 2017 to August 2017. Seventy-one patients undergoing elective laparoscopic colorectal surgery with healthy lung condition were randomly allocated into the three groups based on the concentration of inspired oxygen applied during anaesthesia emergence: 40%-, 80%- or 100%-oxygen. End-expiratory lung impedance (EELI) with normal tidal ventilation and total lung impedance (TLI) with full respiratory effort were measured preoperatively and before discharge in the post-anaesthesia care unit by EIT, and perioperative changes (the ratio of difference between preoperative and postoperative value to preoperative value) were compared among the three groups. Postoperative lung impedances were significantly reduced compared with preoperative values in all patients (P < 0.001); however, perioperative lung impedance reduction (%) did not differ among the three oxygen groups. The mean reduction ratio in each 40%-, 80%-, and 100%-oxygen group were 37% ± 13%, 41% ± 14%, and 46% ± 14% for EELI (P = 0.125) and 40% ± 20%, 44% ± 17% and 49% ± 20% for TLI (P = 0.276), respectively. Inspired oxygen concentrations applied during anaesthesia emergence did not show a significant difference in postoperative lung volume as measured by EIT in patients undergoing laparoscopic colorectal surgery with healthy lungs.Trial registration cris.nih.go.kr (KCT0002642).

Deep neural network predicts emotional responses of the human brain from functional magnetic resonance imaging.

An artificial neural network with multiple hidden layers (known as a deep neural network, or DNN) was employed as a predictive model (DNNp) for the first time to predict emotional responses using whole-brain functional magnetic resonance imaging (fMRI) data from individual subjects. During fMRI data acquisition, 10 healthy participants listened to 80 International Affective Digital Sound stimuli and rated their own emotions generated by each sound stimulus in terms of the arousal, dominance, and valence dimensions. The whole-brain spatial patterns from a general linear model (i.e., beta-valued maps) for each sound stimulus and the emotional response ratings were used as the input and output for the DNNP, respectively. Based on a nested five-fold cross-validation scheme, the paired input and output data were divided into training (three-fold), validation (one-fold), and test (one-fold) data. The DNNP was trained and optimized using the training and validation data and was tested using the test data. The Pearson's correlation coefficients between the rated and predicted emotional responses from our DNNP model with weight sparsity optimization (mean ±â€¯standard error 0.52 ±â€¯0.02 for arousal, 0.51 ±â€¯0.03 for dominance, and 0.51 ±â€¯0.03 for valence, with an input denoising level of 0.3 and a mini-batch size of 1) were significantly greater than those of DNN models with conventional regularization schemes including elastic net regularization (0.15 ±â€¯0.05, 0.15 ±â€¯0.06, and 0.21 ±â€¯0.04 for arousal, dominance, and valence, respectively), those of shallow models including logistic regression (0.11 ±â€¯0.04, 0.10 ±â€¯0.05, and 0.17 ±â€¯0.04 for arousal, dominance, and valence, respectively; average of logistic regression and sparse logistic regression), and those of support vector machine-based predictive models (SVMps; 0.12 ±â€¯0.06, 0.06 ±â€¯0.06, and 0.10 ±â€¯0.06 for arousal, dominance, and valence, respectively; average of linear and non-linear SVMps). This difference was confirmed to be significant with a Bonferroni-corrected p-value of less than 0.001 from a one-way analysis of variance (ANOVA) and subsequent paired t-test. The weights of the trained DNNPs were interpreted and input patterns that maximized or minimized the output of the DNNPs (i.e., the emotional responses) were estimated. Based on a binary classification of each emotion category (e.g., high arousal vs. low arousal), the error rates for the DNNP (31.2% ±â€¯1.3% for arousal, 29.0% ±â€¯1.7% for dominance, and 28.6% ±â€¯3.0% for valence) were significantly lower than those for the linear SVMP (44.7% ±â€¯2.0%, 50.7% ±â€¯1.7%, and 47.4% ±â€¯1.9% for arousal, dominance, and valence, respectively) and the non-linear SVMP (48.8% ±â€¯2.3%, 52.2% ±â€¯1.9%, and 46.4% ±â€¯1.3% for arousal, dominance, and valence, respectively), as confirmed by the Bonferroni-corrected p < 0.001 from the one-way ANOVA. Our study demonstrates that the DNNp model is able to reveal neuronal circuitry associated with human emotional processing - including structures in the limbic and paralimbic areas, which include the amygdala, prefrontal areas, anterior cingulate cortex, insula, and caudate. Our DNNp model was also able to use activation patterns in these structures to predict and classify emotional responses to stimuli.

Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback.

The triple networks, namely the default-mode network (DMN), the central executive network (CEN), and the salience network (SN), play crucial roles in disorders of the brain, as well as in basic neuroscientific processes such as mindfulness. However, currently, there is no consensus on the underlying functional features of the triple networks associated with mindfulness. In this study, we tested the hypothesis that (a) the partial regression coefficient (i.e., slope): from the SN to the DMN, mediated by the CEN, would be one of the potential mindfulness features in the real-time functional magnetic resonance imaging (rtfMRI) neurofeedback (NF) setting, and (b) this slope level may be enhanced by rtfMRI-NF training. Sixty healthy mindfulness-naïve males participated in an MRI session consisting of two non-rtfMRI-runs, followed by two rtfMRI-NF runs and one transfer run. Once the regions-of-interest of each of the triple networks were defined using the non-rtfMRI-runs, the slope level was calculated by mediation analysis and used as neurofeedback information, in the form of a thermometer bar, to assist with participant mindfulness during the rtfMRI-NF runs. The participants were asked to increase the level of the thermometer bar while deploying a mindfulness strategy, which consisted of focusing attention on the physical sensations of breathing. rtfMRI-NF training was conducted as part of a randomized controlled trial design, in which participants were randomly assigned to either an experimental group or a control group. The participants in the experimental group received contingent neurofeedback information, which was obtained from their own brain signals, whereas the participants in the control group received non-contingent neurofeedback information that originated from matched participants in the experimental group. Our results indicated that the slope level from the SN to the DMN, mediated by the CEN, was associated with mindfulness score (rtfMRI-NF runs: r = 0.53, p = 0.007; p-value was corrected from 10,000 random permutations) and with task-performance feedback score (rtfMRI-NF run: r = 0.61, p = 0.001) in the experimental group only. In addition, during the rtfMRI-NF runs the level of the partial regression coefficient feature was substantially increased in the experimental group compared to the control group (p < 0.05 from the paired t-test; the p-value was corrected from 10,000 random permutations). To the best of our knowledge, this is the first study to demonstrate a partial regression coefficient feature of mindfulness in the rtfMRI-NF setting obtained by triple network mediation analysis, as well as the possibility of enhancement of the partial regression coefficient feature by rtfMRI-NF training.