Search

Neuroimaging research has been at the forefront of concerns regarding the failure of experimental findings to replicate. In the study of brain-behavior relationships, past failures to find replicable and robust effects have been attributed to methodological shortcomings. Methodological rigor is important, but there are other overlooked possibilities: most published studies share three foundational assumptions, often implicitly, that may be faulty. In this paper, we consider the empirical evidence from human brain imaging and the study of non-human animals that calls each foundational assumption into question. We then consider the opportunities for a robust science of brain-behavior relationships that await if scientists ground their research efforts in revised assumptions supported by current empirical evidence., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system.

For physicians to make rapid clinical decisions for patients with congestive heart failure, the assessment of pulmonary edema severity in chest radiographs is vital. Although deep learning has shown promise in detecting the presence or absence or discrete grades of severity, of such edema, prediction of continuous-valued severity yet remains a challenge. Here, we propose PENet: Siamese convolutional neural networks to assess the continuous spectrum of severity of lung edema from chest radiographs. We present different modes of implementing this network and demonstrate that our best model outperforms that of earlier work (mean AUC of 0.91 over 0.87), while using only 1/16-th the dimension of input images and 1/69-th the size of training data, thus also saving expensive computation.

Graph neural networks (GNN) are an emerging framework in the deep learning community. In most GNN applications, the graph topology of data samples is provided in the dataset. Specifically, the graph shift operator (GSO), which could be adjacency, graph Laplacian, or their normalizations, is known a priori. However we often have no knowledge of the grand-truth graph topology underlying real-world datasets. One example of this is to extract subject-invariant features from physiological electroencephalogram (EEG) to predict a cognitive task. Previous methods use electrode sites to represent a node in the graph and connect them in various ways to hand-engineer a GSO e.g., i) each pair of electrode sites is connected to form a complete graph, ii) a specific number of electrode sites are connected to form a k-nearest neighbor graph, iii) each pair of electrode site is connected only if the Euclidean distance is within a heuristic threshold. In this paper, we overcome this limitation by parameterizing the GSO using a multi-head attention mechanism to explore the functional neural connectivity subject to a cognitive task between different electrode sites, and simultaneously learn the unsupervised graph topology in conjunction with the parameters of graph convolutional kernels.

We investigate a regularization framework for subject transfer learning in which we train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label. We introduce three notions of independence and corresponding penalty terms using mutual information or divergence as a proxy for independence. For each penalty term, we provide several concrete estimation algorithms, using analytic methods as well as neural critic functions. We propose a hands-off strategy for applying this diverse family of regularization schemes to a new dataset, which we call "Auto Transfer". We evaluate the performance of these individual regularization strategies under our AutoTransfer framework on EEG, EMG, and ECoG datasets, showing that these approaches can improve subject transfer learning for challenging real-world datasets.

This study evaluated the feasibility of using occipitoparietal alpha activity to drive target/non-target classification in a brain-computer interface (BCI) for communication. EEG data were collected from 12 participants who completed BCI Rapid Serial Visual Presentation (RSVP) calibrations at two different presentation rates: 1 and 4 Hz. Attention-related changes in posterior alpha activity were compared to two event-related potentials (ERPs): N200 and P300. Machine learning approaches evaluated target/non-target classification accuracy using alpha activity. Results indicated significant alpha attenuation following target letters at both 1 and 4 Hz presentation rates, though this effect was significantly reduced in the 4 Hz condition. Target-related alpha attenuation was not correlated with coincident N200 or P300 target effects. Classification using posterior alpha activity was above chance and benefitted from individualized tuning procedures. These findings suggest that target-related posterior alpha attenuation is detectable in a BCI RSVP calibration and that this signal could be leveraged in machine learning algorithms used for RSVP or comparable attention-based BCI paradigms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Klee, Memmott, Smedemark-Margulies, Celik, Erdogmus and Oken.)

Background and Objectives: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous work has suggested that artificial intelligence may be able to detect incident severe disease (treatment-requiring retinopathy of prematurity [TR-ROP]) before clinical diagnosis. We aimed to build a risk model that combined artificial intelligence with clinical demographics to reduce the number of examinations without missing cases of TR-ROP., Methods: Infants undergoing routine ROP screening examinations (1579 total eyes, 190 with TR-ROP) were recruited from 8 North American study centers. A vascular severity score (VSS) was derived from retinal fundus images obtained at 32 to 33 weeks' postmenstrual age. Seven ElasticNet logistic regression models were trained on all combinations of birth weight, gestational age, and VSS. The area under the precision-recall curve was used to identify the highest-performing model., Results: The gestational age + VSS model had the highest performance (mean ± SD area under the precision-recall curve: 0.35 ± 0.11). On 2 different test data sets (n = 444 and n = 132), sensitivity was 100% (positive predictive value: 28.1% and 22.6%) and specificity was 48.9% and 80.8% (negative predictive value: 100.0%)., Conclusions: Using a single examination, this model identified all infants who developed TR-ROP, on average, >1 month before diagnosis with moderate to high specificity. This approach could lead to earlier identification of incident severe ROP, reducing late diagnosis and treatment while simultaneously reducing the number of ROP examinations and unnecessary physiologic stress for low-risk infants., Competing Interests: POTENTIAL CONFLICT OF INTEREST: Dr Chan is on the Scientific Advisory Board for Phoenix Technology Group (Pleasanton, CA) and is a consultant for Novartis (Basel, Switzerland) and Alcon (Ft Worth, TX). Dr Chiang was previously a consultant for Novartis (Basel, Switzerland) and is an equity owner of Inteleretina (Honolulu, HI). Drs Chiang, Campbell, Chan, and Kalpathy-Cramer receive research support from Genentech. Dr Chan receives research support from Regeneron. The Imaging and Informatics in Retinopathy of Prematurity Deep Learning system has been licensed to Boston Artificial Intelligence laboratories by Oregon Health & Science University, Massachusetts General Hospital, Northeastern University, and the University of Illinois Chicago, which may result in royalties to Drs Chan, Campbell, and Kalpathy-Cramer in the future. Dr Campbell is an unpaid advisor to Boston Artificial Intelligence; the other authors have indicated they have no financial relationships relevant to this article to disclose., (Copyright © 2021 by the American Academy of Pediatrics.)

Autocorrelation in functional MRI (fMRI) time series has been studied for decades, mostly considered as noise in the time series which is removed via prewhitening with an autoregressive model. Recent results suggest that the coefficients of an autoregressive model t to fMRI data may provide an indicator of underlying brain activity, suggesting that prewhitening could be removing important diagnostic information. This paper explores the explanatory value of these autoregressive features extracted from fMRI by considering the use of these features in a classification task. As a point of comparison, functional network based features are extracted from the same data and used in the same classification task. We find that in most cases, network based features provide better classification accuracy. However, using principal component analysis to combine network based features and autoregressive features for classification based on a support vector machine provides improved classification accuracy compared to single features or network features, suggesting that when properly combined there may be additional information to be gained from autoregressive features.

Traumatic brain injury (TBI) is a sudden injury that causes damage to the brain. TBI can have wide-ranging physical, psychological, and cognitive effects. TBI outcomes include acute injuries, such as contusion or hematoma, as well as chronic sequelae that emerge days to years later, including cognitive decline and seizures. Some TBI patients develop posttraumatic epilepsy (PTE), or recurrent and unprovoked seizures following TBI. In recent years, significant efforts have been made to identify biomarkers of epileptogenesis, the process by which a normal brain becomes capable of generating seizures. These biomarkers would allow for a higher standard of care by identifying patients at risk of developing PTE as candidates for antiepileptogenic interventions. In this paper, we use deep neural network architectures to automatically detect potential biomarkers of PTE from electroencephalogram (EEG) data collected between post-injury day 1-7 from patients with moderate-to-severe TBI. Continuous EEG is often part of multimodal monitoring for TBI patients in intensive care units. Clinicians review EEG to identify the presence of epileptiform abnormalities (EAs), such as seizures, periodic discharges, and abnormal rhythmic delta activity, which are potential biomarkers of epileptogenesis. We show that a recurrent neural network trained with continuous EEG data can be used to identify EAs with the highest accuracy of 80.78%, paving the way for robust, automated detection of epileptiform activity in TBI patients.

The electromyography (EMG) signals have been widely utilized in human-robot interaction for extracting user hand/arm motion instructions. A major challenge of the online interaction with robots is the reliable EMG recognition from real-time data. However, previous studies mainly focused on using steady-state EMG signals with a small number of grasp patterns to implement classification algorithms, which is insufficient to generate robust control regarding the dynamic muscular activity variation in practice. Introducing more EMG variability during training and validation could implement a better dynamic-motion detection, but only limited research focused on such grasp-movement identification, and all of those assessments on the non-static EMG classification require supervised ground-truth label of the movement status. In this study, we propose a framework for classifying EMG signals generated from continuous grasp movements with variations on dynamic arm/hand postures, using an unsupervised motion status segmentation method. We collected data from large gesture vocabularies with multiple dynamic motion phases to encode the transitions from one intent to another based on common sequences of the grasp movements. Two classifiers were constructed for identifying the motion-phase label and grasptype label, where the dynamic motion phases were segmented and labeled in an unsupervised manner. The proposed framework was evaluated in real-time with the accuracy variation over time presented, which was shown to be efficient due to the high degree of freedom of the EMG data.

Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore, we develop various graph neural network (GNN) models that project electrodes onto the nodes of a graph, where the node features are represented as EEG channel samples collected over a trial, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework outperforms standard CNN classifiers across ErrP, and RSVP datasets, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Another practical advantage of our GNN-based framework is that it can be used in EEG channel selection, which is critical for reducing computational cost, and designing portable EEG headsets.

Modeling biological dynamical systems is challenging due to the interdependence of different system components, some of which are not fully understood. To fill existing gaps in our ability to mechanistically model physiological systems, we propose to combine neural networks with physics-based models. Specifically, we demonstrate how we can approximate missing ordinary differential equations (ODEs) coupled with known ODEs using Bayesian filtering techniques to train the model parameters and simultaneously estimate dynamic state variables. As a study case we leverage a well-understood model for blood circulation in the human retina and replace one of its core ODEs with a neural network approximation, representing the case where we have incomplete knowledge of the physiological state dynamics. Results demonstrate that state dynamics corresponding to the missing ODEs can be approximated well using a neural network trained using a recursive Bayesian filtering approach in a fashion coupled with the known state dynamic differential equations. This demonstrates that dynamics and impact of missing state variables can be captured through joint state estimation and model parameter estimation within a recursive Bayesian state estimation (RBSE) framework. Results also indicate that this RBSE approach to training the NN parameters yields better outcomes (measurement/state estimation accuracy) than training the neural network with backpropagation through time in the same setting.

For the last several decades, emotion research has attempted to identify a "biomarker" or consistent pattern of brain activity to characterize a single category of emotion (e.g., fear) that will remain consistent across all instances of that category, regardless of individual and context. In this study, we investigated variation rather than consistency during emotional experiences while people watched video clips chosen to evoke instances of specific emotion categories. Specifically, we developed a sequential probabilistic approach to model the temporal dynamics in a participant's brain activity during video viewing. We characterized brain states during these clips as distinct state occupancy periods between state transitions in blood oxygen level dependent (BOLD) signal patterns. We found substantial variation in the state occupancy probability distributions across individuals watching the same video, supporting the hypothesis that when it comes to the brain correlates of emotional experience, variation may indeed be the norm.

Purpose: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness related to oxygen exposure in premature infants. Since oxygen monitoring protocols have reduced the incidence of treatment-requiring ROP (TR-ROP), it remains unclear whether oxygen exposure remains a relevant risk factor for incident TR-ROP and aggressive ROP (A-ROP), a severe, rapidly progressing form of ROP. The purpose of this proof-of-concept study was to use electronic health record (EHR) data to evaluate early oxygen exposure as a predictive variable for developing TR-ROP and A-ROP., Design: Retrospective cohort study., Participants: Two hundred forty-four infants screened for ROP at a single academic center., Methods: For each infant, oxygen saturations and fraction of inspired oxygen (FiO 2 ) were extracted manually from the EHR until 31 weeks postmenstrual age (PMA). Cumulative minimum, maximum, and mean oxygen saturation and FiO 2 were calculated on a weekly basis. Random forest models were trained with 5-fold cross-validation using gestational age (GA) and cumulative minimum FiO 2 at 30 weeks PMA to identify infants who developed TR-ROP. Secondary receiver operating characteristic (ROC) curve analysis of infants with or without A-ROP was performed without cross-validation because of small numbers., Main Outcome Measures: For each model, cross-validation performance for incident TR-ROP was assessed using area under the ROC curve (AUC) and area under the precision-recall curve (AUPRC) scores. For A-ROP, we calculated AUC and evaluated sensitivity and specificity at a high-sensitivity operating point., Results: Of the 244 infants included, 33 developed TR-ROP, of which 5 developed A-ROP. For incident TR-ROP, random forest models trained on GA plus cumulative minimum FiO 2 (AUC = 0.93 ± 0.06; AUPRC = 0.76 ± 0.08) were not significantly better than models trained on GA alone (AUC = 0.92 ± 0.06 [ P  = 0.59]; AUPRC = 0.74 ± 0.12 [ P  = 0.32]). Models using oxygen alone showed an AUC of 0.80 ± 0.09. ROC analysis for A-ROP found an AUC of 0.92 (95% confidence interval, 0.87-0.96)., Conclusions: Oxygen exposure can be extracted from the EHR and quantified as a risk factor for incident TR-ROP and A-ROP. Extracting quantifiable clinical features from the EHR may be useful for building risk models for multiple diseases and evaluating the complex relationships among oxygen exposure, ROP, and other sequelae of prematurity., (© 2021 by the American Academy of Ophthalmology.)

Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological biosignals often vary across users and recording sessions due to unstable physical/mental conditions and task-irrelevant activities. To deal with this challenge, we propose a method of adversarial feature encoding with the concept of a Rateless Autoencoder (RAE), in order to exploit disentangled, nuisance-robust, and universal representations. We achieve a good trade-off between user-specific and task-relevant features by making use of the stochastic disentanglement of the latent representations by adopting additional adversarial networks. The proposed model is applicable to a wider range of unknown users and tasks as well as different classifiers. Results on cross-subject transfer evaluations show the advantages of the proposed framework, with up to an 11.6% improvement in the average subject-transfer classification accuracy.

Objective: Transfusion of blood and blood components is a special type of tissue transplantation, a life-saving treatment. However, besides the benefits of blood product transfusions, there are also some undesirable side effects. In the present study, the frequency and type of transfusion reactions related to blood and blood components were investigated., Methods: In this retrospective study, types and the time of occurrence of acute transfusion reaction (ATR), the types of blood and blood components used in our hospital between January 2018 and January 2020 were evaluated for hemovigilance using unit blood products application and side effect reporting forms., Results: During the 2-year period, 61,636 blood and/or blood components were used in 9334 patients, and 53 of the transfused patients developed ATR. In two patients, ATR developed 2 times and a total of 55 ATR developed. Of the patients who developed ATR, 18 were female and 35 were male, and their ages ranged from 1 month to 85 years. The frequency of ATR was 0.09%, and 47.3% of ATRs were allergic transfusion reactions, 41.8% were febrile non-hemolytic transfusion reactions (FNHTRs), 7.3% were hypotensive transfusion reactions, and 1.8% were transfusion-related lung injury. Fifty-five ATRs were found to be associated with 61.8% erythrocyte suspension, 30.9% with FFP, 5.5% with platelet suspension, and 1.8% with whole blood., Conclusion: There are a limited number of studies evaluating transfusion reaction frequency and reaction types from our country. The most frequent ATR reported in our hospital were mild allergic reactions and FNHTR. The most common side effect of blood product type was erythrocyte suspension. It is important to monitor the transfused patients for undesired reactions during and after the transfusion to determine the frequency, type, risk factors, and safety precautions of the transfusion reactions., Competing Interests: Conflict of Interest: No conflict of interest was declared by the authors., (Copyright: © 2021 by Istanbul Northern Anatolian Association of Public Hospitals.)

Handedness has been associated with behavioral asymmetries between limbs that suggest specialized function of dominant and non-dominant hand. Whether patterns of muscle co-activation, representing muscle synergies, also differ between the limbs remains an open question. Previous investigations of proximal upper limb muscle synergies have reported little evidence of limb asymmetry; however, whether the same is true of the distal upper limb and hand remains unknown. This study compared forearm and hand muscle synergies between the dominant and non-dominant limb of left-handed and right-handed participants. Participants formed their hands into the postures of the American Sign Language (ASL) alphabet, while EMG was recorded from hand and forearm muscles. Muscle synergies were extracted for each limb individually by applying non-negative-matrix-factorization (NMF). Extracted synergies were compared between limbs for each individual, and between individuals to assess within and across participant differences. Results indicate no difference between the limbs for individuals, but differences in limb synergies at the population level. Left limb synergies were found to be more similar than right limb synergies across left- and right-handed individuals. Synergies of the left hand of left dominant individuals were found to have greater population level similarity than the other limbs tested. Results are interpreted with respect to known differences in the neuroanatomy and neurophysiology of proximal and distal upper limb motor control. Implications for skill training in sports requiring dexterous control of the hand are discussed., (© 2021 Don Liang, Mathew Yarossi, Spencer Lake Jacobs-Skolik, Mariusz P. Furmanek, Dana Brooks, Deniz Erdogmus, Eugene Tunik, published by Sciendo.)

Transcranial Magnetic Stimulation (TMS) can be used to map cortical motor topography by spatially sampling the sensorimotor cortex while recording Motor Evoked Potentials (MEP) with surface electromyography (EMG). Traditional sampling strategies are time-consuming and inefficient, as they ignore the fact that responsive sites are typically sparse and highly spatially correlated. An alternative approach, commonly employed when TMS mapping is used for presurgical planning, is to leverage the expertise of the coil operator to use MEPs elicited by previous stimuli as feedback to decide which loci to stimulate next. In this paper, we propose to automatically infer optimal future stimulus loci using active learning Gaussian Process-based sampling in place of user expertise. We first compare the user-guided (USRG) method to the traditional grid selection method and randomized sampling to verify that the USRG approach has superior performance. We then compare several novel active Gaussian Process (GP) strategies with the USRG approach. Experimental results using real data show that, as expected, the USRG method is superior to the grid and random approach in both time efficiency and MEP map accuracy. We also found that an active warped GP entropy and a GP random-based strategy performed equally as well as, or even better than, the USRG method. These methods were completely automatic, and succeeded in efficiently sampling the regions in which the MEP response variations are largely confined. This work provides the foundation for highly efficient, fully automatized TMS mapping, especially when considered in the context of advances in robotic coil operation.

Trial-by-trial texture classification analysis and identifying salient texture related EEG features during active touch that are minimally influenced by movement type and frequency conditions are the main contributions of this work. A total of twelve healthy subjects were recruited. Each subject was instructed to use the fingertip of their dominant hand's index finger to rub or tap three textured surfaces (smooth flat, medium rough, and rough) with three levels of movement frequency (approximately 2, 1 and 0.5 Hz). EEG and force data were collected synchronously during each touch condition. A systematic feature selection process was performed to select temporal and spectral EEG features that contribute to texture classification but have low contribution towards movement type and frequency classification. A tenfold cross validation was used to train two 3-class (each for texture and movement frequency classification) and a 2-class (movement type) Support Vector Machine classifiers. Our results showed that the total power in the mu (8-15 Hz) and beta (16-30 Hz) frequency bands showed high accuracy in discriminating among textures with different levels of roughness (average accuracy > 84%) but lower contribution towards movement type (average accuracy < 65%) and frequency (average accuracy < 58%) classification.

Access to communication is critical for individuals with late-stage amyotrophic lateral sclerosis (ALS) and minimal volitional movement, but they sometimes present with concomitant visual or ocular motility impairments that affect their performance with eye tracking or visual brain-computer interface (BCI) systems. In this study, we explored the use of modified eye tracking and steady state visual evoked potential (SSVEP) BCI, in combination with the Shuffle Speller typing interface, for this population. Two participants with late-stage ALS, visual impairments, and minimal volitional movement completed a single-case experimental research design comparing copy-spelling performance with three different typing systems: (1) commercially available eye tracking communication software, (2) Shuffle Speller with modified eye tracking, and (3) Shuffle Speller with SSVEP BCI. Participant 1 was unable to type any correct characters with the commercial system, but achieved accuracies of up to 50% with Shuffle Speller eye tracking and 89% with Shuffle Speller BCI. Participant 2 also had higher maximum accuracies with Shuffle Speller, typing with up to 63% accuracy with eye tracking and 100% accuracy with BCI. However, participants' typing accuracy for both Shuffle Speller conditions was highly variable, particularly in the BCI condition. Both the Shuffle Speller interface and SSVEP BCI input show promise for improving typing performance for people with late-stage ALS. Further development of innovative BCI systems for this population is needed., (Copyright © 2020 Peters, Bedrick, Dudy, Eddy, Higger, Kinsella, McLaughlin, Memmott, Oken, Quivira, Spaulding, Erdogmus and Fried-Oken.)

Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to explore this question. In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to attempt to discover biomarkers in the brain or body for the corresponding emotion categories. This practice relies on the assumption that the labels refer to objective categories that can be discovered. Here, we critically examine this approach across three distinct datasets collected during emotional episodes-measuring the human brain, body, and subjective experience-and compare supervised classification solutions with those from unsupervised clustering in which no labels are assigned to the data. We conclude with a set of recommendations to guide researchers towards meaningful, data-driven discoveries in the science of emotion and beyond.

Currently, myoelectric prostheses lack dexterity and ease of control, in part because of inadequate schemes to extract relevant muscle features that can approximate muscle activation patterns that enable individuated dexterous finger motion. This project seeks to apply a novel algorithm pipeline that extracts muscle activation patterns from one limb, as well as from forearm muscles of the opposite limb, to predict muscle activation data of opposite limb intrinsic hand muscles, with the long-range goal of informing dexterous prosthetic control.

Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways. One major challenge of physiological status assessment is the problem of transfer learning caused by the domain inconsistency of biosignals across users or different recording sessions from the same user. We propose an adversarial inference approach for transfer learning to extract disentangled nuisance-robust representations from physiological biosignal data in stress status level assessment. We exploit the trade-off between task-related features and person-discriminative information by using both an adversary network and a nuisance network to jointly manipulate and disentangle the learned latent representations by the encoder, which are then input to a discriminative classifier. Results on cross-subjects transfer evaluations demonstrate the benefits of the proposed adversarial framework, and thus show its capabilities to adapt to a broader range of subjects. Finally we highlight that our proposed adversarial transfer learning approach is also applicable to other deep feature learning frameworks.

Purpose: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images., Methods: We developed two datasets containing 100 and 5512 posterior retinal images, respectively. After segmenting retinal vessels, we detected the vessel centerlines. Then, we extracted features relevant to ROP, including tortuosity and dilation measures, and used these features in the classifiers including logistic regression, support vector machine and neural networks to assess a severity score for the input. We tested our system with fivefold cross-validation and calculated the area under the curve (AUC) metric for each classifier and dataset., Results: For predicting plus versus not-plus categories, we achieved 99% and 94% AUC on the first and second datasets, respectively. For predicting pre-plus or worse versus normal categories, we achieved 99% and 88% AUC on the first and second datasets, respectively. The CNN method achieved 98% and 94% for predicting two categories on the second dataset., Conclusions: Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN-like performance., Translational Relevance: The high performance of I-ROP ASSIST suggests potential applications in automated and objective diagnosis of plus disease., Competing Interests: Disclosure: V.M. Yildiz, None; P. Tian, None; I. Yildiz, None; J.M. Brown, None; J. Kalpathy-Cramer, None; J. Dy, None; S. Ioannidis, None; D. Erdogmus, None; S. Ostmo, None; S.J. Kim, None; R.V.P. Chan, Visunex Medical Systems Scientific Advisory Board (S), Genentech (C); J.P. Campbell, None; M.F. Chiang, Clarity Medical Systems Scientific Advisory Board (S), Novartis (C), and Inteleretina (I), (Copyright 2020 The Authors.)

Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential sub-optimality of ranking-based feature selection by any criterion, we propose to extend this focus with an information theoretic learning-driven feature transformation concept., Methods: We present a maximum mutual information linear transformation and a nonlinear transformation framework derived by a general definition of the feature transformation learning problem. Empirical assessments are performed based on electroencephalographic data recorded during a four class motor imagery brain-computer interface (BCI) task. Exploiting the state-of-the-art methods for initial feature vector construction, we compare the proposed approaches with conventional feature selection-based dimensionality reduction techniques, which are widely used in brain interfaces. Furthermore, for the multi-class problem, we present and exploit a hierarchical graphical model-based BCI decoding system., Results: Both binary and multi-class decoding analyses demonstrate significantly better performances with the proposed methods., Conclusion: Information theoretic feature transformations are capable of tackling potential confounders of conventional approaches in various settings., Significance: We argue that this concept provides significant insights to extend the focus on feature selection heuristics to a broader definition of feature transformation learning in brain interfaces.

Importance: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective and qualitative., Objective: To describe a quantitative ROP severity score derived using a deep learning algorithm designed to evaluate plus disease and to assess its utility for objectively monitoring ROP progression., Design, Setting, and Participants: This retrospective cohort study included images from 5255 clinical examinations of 871 premature infants who met the ROP screening criteria of the Imaging and Informatics in ROP (i-ROP) Consortium, which comprises 9 tertiary care centers in North America, from July 1, 2011, to December 31, 2016. Data analysis was performed from July 2017 to May 2018., Exposure: A deep learning algorithm was used to assign a continuous ROP vascular severity score from 1 (most normal) to 9 (most severe) at each examination based on a single posterior photograph compared with a reference standard diagnosis (RSD) simplified into 4 categories: no ROP, mild ROP, type 2 ROP or pre-plus disease, or type 1 ROP. Disease course was assessed longitudinally across multiple examinations for all patients., Main Outcomes and Measures: Mean ROP vascular severity score progression over time compared with the RSD., Results: A total of 5255 clinical examinations from 871 infants (mean [SD] gestational age, 27.0 [2.0] weeks; 493 [56.6%] male; mean [SD] birth weight, 949 [271] g) were analyzed. The median severity scores for each category were as follows: 1.1 (interquartile range [IQR], 1.0-1.5) (no ROP), 1.5 (IQR, 1.1-3.4) (mild ROP), 4.6 (IQR, 2.4-5.3) (type 2 and pre-plus), and 7.5 (IQR, 5.0-8.7) (treatment-requiring ROP) (P < .001). When the long-term differences in the median severity scores across time between the eyes progressing to treatment and those who did not eventually require treatment were compared, the median score was higher in the treatment group by 0.06 at 30 to 32 weeks, 0.75 at 32 to 34 weeks, 3.56 at 34 to 36 weeks, 3.71 at 36 to 38 weeks, and 3.24 at 38 to 40 weeks postmenstrual age (P < .001 for all comparisons)., Conclusions and Relevance: The findings suggest that the proposed ROP vascular severity score is associated with category of disease at a given point in time and clinical progression of ROP in premature infants. Automated image analysis may be used to quantify clinical disease progression and identify infants at high risk for eventually developing treatment-requiring ROP. This finding has implications for quality and delivery of ROP care and for future approaches to disease classification.

Importance: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but treatment failure and disease recurrence are important causes of adverse outcomes in patients with treatment-requiring ROP (TR-ROP)., Objectives: To apply an automated ROP vascular severity score obtained using a deep learning algorithm and to assess its utility for objectively monitoring ROP regression after treatment., Design, Setting, and Participants: This retrospective cohort study used data from the Imaging and Informatics in ROP consortium, which comprises 9 tertiary referral centers in North America that screen high volumes of at-risk infants for ROP. Images of 5255 clinical eye examinations from 871 infants performed between July 2011 and December 2016 were assessed for eligibility in the present study. The disease course was assessed with time across the numerous examinations for patients with TR-ROP. Infants born prematurely meeting screening criteria for ROP who developed TR-ROP and who had images captured within 4 weeks before and after treatment as well as at the time of treatment were included., Main Outcomes and Measures: The primary outcome was mean (SD) ROP vascular severity score before, at time of, and after treatment. A deep learning classifier was used to assign a continuous ROP vascular severity score, which ranged from 1 (normal) to 9 (most severe), at each examination. A secondary outcome was the difference in ROP vascular severity score among eyes treated with laser or the vascular endothelial growth factor antagonist bevacizumab. Differences between groups for both outcomes were assessed using unpaired 2-tailed t tests with Bonferroni correction., Results: Of 5255 examined eyes, 91 developed TR-ROP, of which 46 eyes met the inclusion criteria based on the available images. The mean (SD) birth weight of those patients was 653 (185) g, with a mean (SD) gestational age of 24.9 (1.3) weeks. The mean (SD) ROP vascular severity scores significantly increased 2 weeks prior to treatment (4.19 [1.75]), peaked at treatment (7.43 [1.89]), and decreased for at least 2 weeks after treatment (4.00 [1.88]) (all P < .001). Eyes requiring retreatment with laser had higher ROP vascular severity scores at the time of initial treatment compared with eyes receiving a single treatment (P < .001)., Conclusions and Relevance: This quantitative ROP vascular severity score appears to consistently reflect clinical disease progression and posttreatment regression in eyes with TR-ROP. These study results may have implications for the monitoring of patients with ROP for treatment failure and disease recurrence and for determining the appropriate level of disease severity for primary treatment in eyes with aggressive disease.

Unpredictable and potentially dangerous aggressive behavior by youth with Autism Spectrum Disorder (ASD) can isolate them from foundational educational, social, and familial activities, thereby markedly exacerbating morbidity and costs associated with ASD. This study investigates whether preceding physiological and motion data measured by a wrist-worn biosensor can predict aggression to others by youth with ASD. We recorded peripheral physiological (cardiovascular and electrodermal activity) and motion (accelerometry) signals from a biosensor worn by 20 youth with ASD (ages 6-17 years, 75% male, 85% minimally verbal) during 69 independent naturalistic observation sessions with concurrent behavioral coding in a specialized inpatient psychiatry unit. We developed prediction models based on ridge-regularized logistic regression. Our results suggest that aggression to others can be predicted 1 min before it occurs using 3 min of prior biosensor data with an average area under the curve of 0.71 for a global model and 0.84 for person-dependent models. The biosensor was well tolerated, we obtained useable data in all cases, and no users withdrew from the study. Relatively high predictive accuracy was achieved using antecedent physiological and motion data. Larger trials are needed to further establish an ideal ratio of measurement density to predictive accuracy and reliability. These findings lay the groundwork for the future development of precursor behavior analysis and just-in-time adaptive intervention systems to prevent or mitigate the emergence, occurrence, and impact of aggression in ASD. Autism Res 2019, 12: 1286-1296. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Unpredictable aggression can create a barrier to accessing community, therapeutic, medical, and educational services. The present study evaluated whether data from a wearable biosensor can be used to predict aggression to others by youth with autism spectrum disorder (ASD). Results demonstrate that aggression to others can be predicted 1 min before it occurs with high accuracy, laying the groundwork for the future development of preemptive behavioral interventions and just-in-time adaptive intervention systems to prevent or mitigate the emergence, occurrence, and impact of aggression to others in ASD., (© 2019 International Society for Autism Research, Wiley Periodicals, Inc.)

Electroencephalography (EEG) is an effective non-invasive measurement method to infer user intent in brain-computer interface (BCI) systems for control and communication, however, these systems often lack sufficient accuracy and speed due to low separability of class-conditional EEG feature distributions. Many factors impact system performance, including inadequate training datasets and models' ignorance of the temporal dependency of brain responses to serial stimuli. Here, we propose a signal model for event-related responses in the EEG evoked with a rapid sequence of stimuli in BCI applications. The model describes the EEG as a superposition of impulse responses time-locked to stimuli corrupted with an autoregressive noise process. The performance of the signal model is assessed in the context of RSVP keyboard, a language-model-assisted EEG-based BCI for typing. EEG data obtained for model calibration from 10 healthy participants are used to fit and compare two models: the proposed sequence-based EEG model and the trial-based feature-class-conditional distribution model that ignores temporal dependencies, which has been used in the previous work. The simulation studies indicate that the earlier model that ignores temporal dependencies may be causing drastic reductions in achievable information transfer rate (ITR). Furthermore, the proposed model, with better regularization, may achieve improved accuracy with fewer calibration data samples, potentially helping to reduce calibration time. Specifically, results show an average 8.6% increase in (cross-validated) calibration AUC for a single channel of EEG, and 54% increase in the ITR in a typing task.

With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based methods that are trained to find the 3-D position of arbitrarily-oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3-D rotations and translations using image features. We use and compare mean square error and geodesic loss to train regression CNNs for 3-D pose estimation used in two different scenarios: slice-to-volume registration and volume-to-volume registration. As an exemplary application, we applied the proposed methods to register arbitrarily oriented reconstructed images of fetuses scanned in-utero at a wide gestational age range to a standard atlas space. Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization achieved 3-D pose estimation with a wide capture range in real-time (<100ms). We also tested the generalization capability of the trained CNNs on an expanded age range and on images of newborn subjects with similar and different MR image contrasts. We trained our models on T2-weighted fetal brain MRI scans and used them to predict the 3-D pose of newborn brains based on T1-weighted MRI scans. We showed that the trained models generalized well for the new domain when we performed image contrast transfer through a conditional generative adversarial network. This indicates that the domain of application of the trained deep regression CNNs can be further expanded to image modalities and contrasts other than those used in training. A combination of our proposed methods with accelerated optimization-based registration algorithms can dramatically enhance the performance of automatic imaging devices and image processing methods of the future.

Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training such networks raises when data is unbalanced, which is common in many medical imaging applications such as lesion segmentation where lesion class voxels are often much lower in numbers than non-lesion voxels. A trained network with unbalanced data may make predictions with high precision and low recall, being severely biased towards the non-lesion class which is particularly undesired in most medical applications where false negatives are actually more important than false positives. Various methods have been proposed to address this problem including two step training, sample re-weighting, balanced sampling, and more recently similarity loss functions, and focal loss. In this work we trained fully convolutional deep neural networks using an asymmetric similarity loss function to mitigate the issue of data imbalance and achieve much better trade-off between precision and recall. To this end, we developed a 3D fully convolutional densely connected network (FC-DenseNet) with large overlapping image patches as input and an asymmetric similarity loss layer based on Tversky index (using F β scores). We used large overlapping image patches as inputs for intrinsic and extrinsic data augmentation, a patch selection algorithm, and a patch prediction fusion strategy using B-spline weighted soft voting to account for the uncertainty of prediction in patch borders. We applied this method to multiple sclerosis (MS) lesion segmentation based on two different datasets of MSSEG 2016 and ISBI longitudinal MS lesion segmentation challenge, where we achieved average Dice similarity coefficients of 69.9% and 65.74%, respectively, achieving top performance in both challenges. We compared the performance of our network trained with F β loss, focal loss, and generalized Dice loss (GDL) functions. Through September 2018 our network trained with focal loss ranked first according to the ISBI challenge overall score and resulted in the lowest reported lesion false positive rate among all submitted methods. Our network trained with the asymmetric similarity loss led to the lowest surface distance and the best lesion true positive rate that is arguably the most important performance metric in a clinical decision support system for lesion detection. The asymmetric similarity loss function based on F β scores allows training networks that make a better balance between precision and recall in highly unbalanced image segmentation. We achieved superior performance in MS lesion segmentation using a patchwise 3D FC-DenseNet with a patch prediction fusion strategy, trained with asymmetric similarity loss functions.

Background: Prior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis., Methods: Clinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1-9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity., Results: 4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001)., Conclusion: The i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening., Competing Interests: Competing interests: MFC is an unpaid member of the Scientific Advisory Board for Clarity Medical Systems (Pleasanton, California, USA), a Consultant for Novartis (Basel, Switzerland) and an initial member of Inteleretina, LLC (Honolulu, Hawaii, USA). RVPC is a Scientific Advisory Board member for Visunex Medical Systems (Fremont, California, USA) and a Consultant for Alcon (Fort Worth, Texas, USA), Allergan (Irvine, California, USA) and Bausch and Lomb (St. Louis, Missouri, USA). JPC is a consultant to Allergan (Irvine, California, USA)., (© Author(s) (or their employer(s)) 2018. No commercial re-use. See rights and permissions. Published by BMJ.)

The task of labeling samples is demanding and expensive. Active learning aims to generate the smallest possible training data set that results in a classifier with high performance in the test phase. It usually consists of two steps of selecting a set of queries and requesting their labels. Among the suggested objectives to score the query sets, information theoretic measures have become very popular. Yet among them, those based on Fisher information (FI) have the advantage of considering the diversity among the queries and tractable computations. In this work, we provide a practical algorithm based on Fisher information ratio to obtain query distribution for a general framework where, in contrast to the previous FI-based querying methods, we make no assumptions over the test distribution. The empirical results on synthetic and real-world data sets indicate that this algorithm gives competitive results.

Augmentative and alternative communication (AAC) is typically used by people with severe speech and physical disabilities (SSPI) and is one of the main application areas for brain computer interface (BCI) technology. The target population includes people with cerebral palsy (CP), amyotrophic lateral sclerosis (ALS) and locked-in-syndrome (LIS). Word-based AAC systems are mainly faster than letter-based counterparts and are usually supplemented by icons to aid the users. Those iconbased AAC systems that use binary signaling methods such as single click can convert into a single input BCI systems such as ERP detection. Matrix speller paradigm are typically used to help users identify their target icon on the screen, however it ties screen space to vocabulary size and navigation complexity, which may require users to make repetitive head, neck, or eye movements to visually locate their intended targets on the screen. Rapid serial visual presentation (RSVP) is an alternative interface that minimizes required movement by displaying all icons at a fix location, one at a time. IconMessenger is an icon-based BCI-AAC system that combines ERP signal detection with a unified framework for different presentation paradigms including RSVP, matrix speller row&column presentation (RCP) and matrix speller single character presentation (SCP). Icon- Messenger also take advantage of a unique sem-gram language model, incorporated tightly in the inference engine. In this study, we assess the ERP shape, classification accuracy and typing performance of different presentation paradigms on 10 healthy participants.

Even with state-of-the-art techniques there are individuals whose paralysis prevents them from communicating with others. Brain⁻Computer-Interfaces (BCI) aim to utilize brain waves to construct a voice for those whose needs remain unmet. In this paper we compare the efficacy of a BCI input signal, code-VEP via Electroencephalography, against eye gaze tracking, among the most popular modalities used. These results, on healthy individuals without paralysis, suggest that while eye tracking works well for some, it does not work well or at all for others; the latter group includes individuals with corrected vision or those who squint their eyes unintentionally while focusing on a task. It is also evident that the performance of the interface is more sensitive to head/body movements when eye tracking is used as the input modality, compared to using c-VEP. Sensitivity to head/body movement could be better in eye tracking systems which are tracking the head or mounted on the face and are designed specifically as assistive devices. The sample interface developed for this assessment has the same reaction time when driven with c-VEP or with eye tracking; approximately 0.5⁻1 second is needed to make a selection among the four options simultaneously presented. Factors, such as system reaction time and robustness play a crucial role in participant preferences.

Electroencephalogram (EEG) signals have been shown very effective for inferring user intents in brain-computer interface (BCI) applications. However, existing EEG-based BCIs, in many cases, lack sufficient performance due to utilizing classifiers that operate on EEG signals induced by individual trials. While many factors influence the classification performance, an important aspect that is often ignored is the temporal dependency of these trial-EEG signals, in some cases impacted by interference of brain responses to consecutive target and non-target trials. In this study, the EEG signals are analyzed in a parametric sequence-based fashion, which considers all trials that induce brain responses in a rapid-sequence fashion, including a mixture of consecutive target and non-target trials. EEG signals are described as a linear combination of time-shifted cortical source activities plus measurement noise. Using a superposition of time invariant with an auto-regressive (AR) process, EEG signals are treated as a linear combination of a stationary Gaussian process and time-locked impulse responses to the stimulus (input events) onsets. The model performance is assessed in the framework of a rapid serial visualization presentation (RSVP) based typing task for three healthy subjects across two sessions. Signal modeling in this fashion yields promising performance outcomes considering a single EEG channel to estimate the user intent.

Importance: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The decision to treat is primarily based on the presence of plus disease, defined as dilation and tortuosity of retinal vessels. However, clinical diagnosis of plus disease is highly subjective and variable., Objective: To implement and validate an algorithm based on deep learning to automatically diagnose plus disease from retinal photographs., Design, Setting, and Participants: A deep convolutional neural network was trained using a data set of 5511 retinal photographs. Each image was previously assigned a reference standard diagnosis (RSD) based on consensus of image grading by 3 experts and clinical diagnosis by 1 expert (ie, normal, pre-plus disease, or plus disease). The algorithm was evaluated by 5-fold cross-validation and tested on an independent set of 100 images. Images were collected from 8 academic institutions participating in the Imaging and Informatics in ROP (i-ROP) cohort study. The deep learning algorithm was tested against 8 ROP experts, each of whom had more than 10 years of clinical experience and more than 5 peer-reviewed publications about ROP. Data were collected from July 2011 to December 2016. Data were analyzed from December 2016 to September 2017., Exposures: A deep learning algorithm trained on retinal photographs., Main Outcomes and Measures: Receiver operating characteristic analysis was performed to evaluate performance of the algorithm against the RSD. Quadratic-weighted κ coefficients were calculated for ternary classification (ie, normal, pre-plus disease, and plus disease) to measure agreement with the RSD and 8 independent experts., Results: Of the 5511 included retinal photographs, 4535 (82.3%) were graded as normal, 805 (14.6%) as pre-plus disease, and 172 (3.1%) as plus disease, based on the RSD. Mean (SD) area under the receiver operating characteristic curve statistics were 0.94 (0.01) for the diagnosis of normal (vs pre-plus disease or plus disease) and 0.98 (0.01) for the diagnosis of plus disease (vs normal or pre-plus disease). For diagnosis of plus disease in an independent test set of 100 retinal images, the algorithm achieved a sensitivity of 93% with 94% specificity. For detection of pre-plus disease or worse, the sensitivity and specificity were 100% and 94%, respectively. On the same test set, the algorithm achieved a quadratic-weighted κ coefficient of 0.92 compared with the RSD, outperforming 6 of 8 ROP experts., Conclusions and Relevance: This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts. This has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.

Individuals with severe speech and physical impairments may have concomitant visual acuity impairments (VAI) or ocular motility impairments (OMI) impacting visual BCI use. We report on the use of the Shuffle Speller typing interface for an SSVEP BCI copy-spelling task under three conditions: simulated VAI, simulated OMI, and unimpaired vision. To mitigate the effect of visual impairments, we introduce a method that adaptively selects a user-specific trial length to maximize expected information transfer rate (ITR); expected ITR is shown to closely approximate the rate of correct letter selections. All participants could type under the unimpaired and simulated VAI conditions, with no significant differences in typing accuracy or speed. Most participants (31 of 37) could not type under the simulated OMI condition; some achieved high accuracy but with slower typing speeds. Reported workload and discomfort were low, and satisfaction high, under the unimpaired and simulated VAI conditions. Implications and future directions to examine effect of visual impairment on BCI use is discussed., Competing Interests: Disclosure of interest No potential conflict of interest was reported by the authors.

Noninvasive EEG (electroencephalography) based auditory attention detection could be useful for improved hearing aids in the future. This work is a novel attempt to investigate the feasibility of online modulation of sound sources by probabilistic detection of auditory attention, using a noninvasive EEG-based brain computer interface. Proposed online system modulates the upcoming sound sources through gain adaptation which employs probabilistic decisions (soft decisions) from a classifier trained on offline calibration data. In this work, calibration EEG data were collected in sessions where the participants listened to two sound sources (one attended and one unattended). Cross-correlation coefficients between the EEG measurements and the attended and unattended sound source envelope (estimates) are used to show differences in sharpness and delays of neural responses for attended versus unattended sound source. Salient features to distinguish attended sources from the unattended ones in the correlation patterns have been identified, and later they have been used to train an auditory attention classifier. Using this classifier, we have shown high offline detection performance with single channel EEG measurements compared to the existing approaches in the literature which employ large number of channels. In addition, using the classifier trained offline in the calibration session, we have shown the performance of the online sound source modulation system. We observe that online sound source modulation system is able to keep the level of attended sound source higher than the unattended source.

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.

Recent findings indicate that brain interfaces have the potential to enable attention-guided auditory scene analysis and manipulation in applications, such as hearing aids and augmented/virtual environments. Specifically, noninvasively acquired electroencephalography (EEG) signals have been demonstrated to carry some evidence regarding, which of multiple synchronous speech waveforms the subject attends to. In this paper, we demonstrate that: 1) using data- and model-driven cross-correlation features yield competitive binary auditory attention classification results with at most 20 s of EEG from 16 channels or even a single well-positioned channel; 2) a model calibrated using equal-energy speech waveforms competing for attention could perform well on estimating attention in closed-loop unbalanced-energy speech waveform situations, where the speech amplitudes are modulated by the estimated attention posterior probability distribution; 3) such a model would perform even better if it is corrected (linearly, in this instance) based on EEG evidence dependence on speech weights in the mixture; and 4) calibrating a model based on population EEG could result in acceptable performance for new individuals/users; therefore, EEG-based auditory attention classifiers may generalize across individuals, leading to reduced or eliminated calibration time and effort.

A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed. Moreover, we conduct real time experiments with human participants to study the human-in-the-loop effect on the performance of the proposed active-RBSE framework and consistent with the simulation results, the results of these experiments show improvement both in typing speed and accuracy.

Crystal structures of adenylate kinase (AdK) from Escherichia coli capture two states: an "open" conformation (apo) obtained in the absence of ligands and a "closed" conformation in which ligands are bound. Other AdK crystal structures suggest intermediate conformations that may lie on the transition pathway between these two states. To characterize the transition from open to closed states in solution, X-ray solution scattering data were collected from AdK in the apo form and with progressively increasing concentrations of five different ligands. Scattering data from apo AdK are consistent with scattering predicted from the crystal structure of AdK in the open conformation. In contrast, data from AdK samples saturated with Ap5A do not agree with that calculated from AdK in the closed conformation. Using cluster analysis of available structures, we selected representative structures in five conformational states: open, partially open, intermediate, partially closed, and closed. We used these structures to estimate the relative abundances of these states for each experimental condition. X-ray solution scattering data obtained from AdK with AMP are dominated by scattering from AdK in the open conformation. For AdK in the presence of high concentrations of ATP and ADP, the conformational ensemble shifts to a mixture of partially open and closed states. Even when AdK is saturated with Ap5A, a significant proportion of AdK remains in a partially open conformation. These results are consistent with an induced-fit model in which the transition of AdK from an open state to a closed state is initiated by ATP binding.

Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data. Experiments with 5 participants yielded an aggregate classification accuracy-intended grasp prediction probability-of 64.5% for 8 different hand gestures, more than 5 times the chance level.

Noninvasive brain computer interfaces (BCI), and more specifically Electroencephalography (EEG) based systems for intent detection need to compensate for the low signal to noise ratio of EEG signals. In many applications, the temporal dependency information from consecutive decisions and contextual data can be used to provide a prior probability for the upcoming decision. In this study we proposed two probabilistic graphical models (PGMs), using context information and previously observed EEG evidences to estimate a probability distribution over the decision space in graph based decision-making mechanism. In this approach, user moves a pointer to the desired vertex in the graph in which each vertex represents an action. To select a vertex, a "Select" command, or a proposed probabilistic Selection criterion (PSC) can be used to automatically detect the user intended vertex. Performance of different PGMs and Selection criteria combinations are compared over a keyboard based on a graph layout. Based on the simulation results, probabilistic Selection criterion along with the probabilistic graphical model provides the highest performance boost for individuals with pour calibration performance and achieving the same performance for individuals with high calibration performance.

Lower extremity function recovery is one of the most important goals in stroke rehabilitation. Many paradigms and technologies have been introduced for the lower limb rehabilitation over the past decades, but their outcomes indicate a need to develop a complementary approach. One attempt to accomplish a better functional recovery is to combine bottom-up and top-down approaches by means of brain-computer interfaces (BCIs). In this study, a BCI-controlled robotic mirror therapy system is proposed for lower limb recovery following stroke. An experimental paradigm including four states is introduced to combine robotic training (bottom-up) and mirror therapy (top-down) approaches. A BCI system is presented to classify the electroencephalography (EEG) evidence. In addition, a probabilistic model is presented to assist patients in transition across the experiment states based on their intent. To demonstrate the feasibility of the system, both offline and online analyses are performed for five healthy subjects. The experiment results show a promising performance for the system, with average accuracy of 94% in offline and 75% in online sessions.

Brain-Computer Interfaces (BCIs) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g., language model priors in spelling applications), accounts for varying brain symbol accuracy and is robust to single brain symbol query errors. This framework is paired with Maximum Mutual Information (MMI) coding which maximizes a generalization of ITR. Both are applicable to any discrete task and brain phenomena (e.g., P300, SSVEP, MI). To demonstrate the efficacy of our approach we perform SSVEP "Shuffle" Speller experiments and compare our recursive coding scheme with traditional decision tree methods including Huffman coding. MMI coding leverages the asymmetry of the classifier's mistakes across a particular user's SSVEP responses; in doing so it offers a 33% increase in letter accuracy though it is 13% slower in our experiment.

We present a method to estimate a multivariate Gaussian distribution of diffusion tensor features in a set of brain regions based on a small sample of healthy individuals, and use this distribution to identify imaging abnormalities in subjects with mild traumatic brain injury. The multivariate model receives apriori knowledge in the form of a neighborhood graph imposed on the precision matrix, which models brain region interactions, and an additional L 1 sparsity constraint. The model is then estimated using the graphical LASSO algorithm and the Mahalanobis distance of healthy and TBI subjects to the distribution mean is used to evaluate the discriminatory power of the model. Our experiments show that the addition of the apriori neighborhood graph results in significant improvements in classification performance compared to a model which does not take into account the brain region interactions or one which uses a fully connected prior graph. In addition, we describe a method, using our model, to detect the regions that contribute the most to the overall abnormality of the DTI profile of a subject's brain., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Purpose: To determine expert agreement on relative retinopathy of prematurity (ROP) disease severity and whether computer-based image analysis can model relative disease severity, and to propose consideration of a more continuous severity score for ROP., Design: We developed 2 databases of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP (i-ROP) cohort study and recruited expert physician, nonexpert physician, and nonphysician graders to classify and perform pairwise comparisons on both databases., Participants: Six participating expert ROP clinician-scientists, each with a minimum of 10 years of clinical ROP experience and 5 ROP publications, and 5 image graders (3 physicians and 2 nonphysician graders) who analyzed images that were obtained during routine ROP screening in neonatal intensive care units., Methods: Images in both databases were ranked by average disease classification (classification ranking), by pairwise comparison using the Elo rating method (comparison ranking), and by correlation with the i-ROP computer-based image analysis system., Main Outcome Measures: Interexpert agreement (weighted κ statistic) compared with the correlation coefficient (CC) between experts on pairwise comparisons and correlation between expert rankings and computer-based image analysis modeling., Results: There was variable interexpert agreement on diagnostic classification of disease (plus, preplus, or normal) among the 6 experts (mean weighted κ, 0.27; range, 0.06-0.63), but good correlation between experts on comparison ranking of disease severity (mean CC, 0.84; range, 0.74-0.93) on the set of 34 images. Comparison ranking provided a severity ranking that was in good agreement with ranking obtained by classification ranking (CC, 0.92). Comparison ranking on the larger dataset by both expert and nonexpert graders demonstrated good correlation (mean CC, 0.97; range, 0.95-0.98). The i-ROP system was able to model this continuous severity with good correlation (CC, 0.86)., Conclusions: Experts diagnose plus disease on a continuum, with poor absolute agreement on classification but good relative agreement on disease severity. These results suggest that the use of pairwise rankings and a continuous severity score, such as that provided by the i-ROP system, may improve agreement on disease severity in the future., (Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)