A Siamese Convolutional Neural Network for Identifying Mild Traumatic Brain Injury and Predicting Recovery

Timely diagnosis of mild traumatic brain injury (mTBI) remains challenging due to the rapid recovery of acute symptoms and the absence of evidence of injury in static neuroimaging scans. Furthermore, while longitudinal tracking of mTBI is essential in understanding how the diseases progresses/regresses over time for enhancing personalized patient care, a standardized approach for this purpose is not yet available. Recent functional neuroimaging studies have provided evidence of brain function alterations following mTBI, suggesting mTBI-detection models can be built based on these changes. Most of these models, however, rely on manual feature engineering, but the optimal set of features for detecting mTBI may be unknown. Data-driven approaches, on the other hand, may uncover hidden relationships in an automated manner, making them suitable for the problem of mTBI detection. This paper presents a data-driven framework based on Siamese Convolutional Neural Network (SCNN) to detect mTBI and to monitor the recovery state from mTBI over time. The proposed framework is tested on the cortical images of Thy1-GCaMP6s mice, obtained via widefield calcium imaging, acquired in a longitudinal study. Results show that the proposed model achieves a classification accuracy of 96.5%. To track the state of the injured brain over time, a reference distance map is constructed, which together with the SCNN model, are employed to assess the recovery state in subsequent sessions after injury, revealing that the recovery progress varies among subjects. The promising results of this work suggest that a similar approach could be potentially applicable for monitoring recovery from mTBI, in humans.

Imaging the large-scale and cellular cortical response to focal traumatic brain injury in mouse neocortex

Traumatic brain injury (TBI) affects neural function at the local injury site and also at distant, connected brain areas. However, the real-time neural dynamics in response to injury and subsequent effects on sensory processing and behavior are not fully resolved, especially across a range of spatial scales. We used in vivo calcium imaging in awake, head-restrained male and female mice to measure large-scale and cellular resolution neuronal activation, respectively, in response to a mild TBI induced by focal controlled cortical impact (CCI) injury of the motor cortex (M1). Widefield imaging revealed an immediate CCI-induced activation at the injury site, followed by a massive slow wave of calcium signal activation that traveled across the majority of the dorsal cortex within approximately 30 s. Correspondingly, two-photon calcium imaging in primary somatosensory cortex (S1) found strong activation of neuropil and neuronal populations during the CCI-induced traveling wave. A depression of calcium signals followed the wave, during which we observed atypical activity of a sparse population of S1 neurons. Longitudinal imaging in the hours and days after CCI revealed increases in the area of whisker-evoked sensory maps at early time points, in parallel to decreases in cortical functional connectivity and behavioral measures. Neural and behavioral changes mostly recovered over hours to days in our mild-TBI model, with a more lasting decrease in the number of active S1 neurons. Our results provide novel spatial and temporal views of neural adaptations that occur at cortical sites remote to a focal brain injury.

An LSTM-based adversarial variational autoencoder framework for self-supervised neural decoding of behavioral choices

Objective. This paper presents data-driven solutions to address two challenges in the problem of linking neural data and behavior: 1) unsupervised analysis of behavioral data and automatic label generation from behavioral observations, and 2) extraction of subject-invariant features for the development of generalized neural decoding models. Approach. For behavioral analysis and label generation, an unsupervised method, which employs an autoencoder to transform behavior data into a cluster-friendly feature space is presented. The model iteratively refines the assigned clusters with soft clustering assignment loss, and gradually improves the learned feature representations. To address subject variability in decoding neural activity, adversarial learning in combination with a long short-term memory-based adversarial variational autoencoder (LSTM-AVAE) model is employed. By using an adversary network to constrain the latent representations, the model captures shared information among subjects' neural activity, making it proper for cross-subject transfer learning. Main results. The proposed approach is evaluated using cortical recordings of Thy1-GCaMP6s transgenic mice obtained via widefield calcium imaging during a motivational licking behavioral experiment. The results show that the proposed model achieves an accuracy of 89.7% in cross-subject neural decoding, outperforming other well-known autoencoder-based feature learning models. These findings suggest that incorporating an adversary network eliminates subject dependency in representations, leading to improved cross-subject transfer learning performance, while also demonstrating the effectiveness of LSTM-based models in capturing the temporal dependencies within neural data. Significance. Results demonstrate the feasibility of the proposed framework in unsupervised clustering and label generation of behavioral data, as well as achieving high accuracy in cross-subject neural decoding, indicating its potentials for relating neural activity to behavior.

The Feeling of Time Passing Is Associated with Recurrent Sustained Activity and Theta Rhythms Across the Cortex

Introduction: We are constantly estimating how much time has passed, and yet know little about the brain mechanisms through which this process occurs. In this pilot study, we evaluated so-called subjective time estimation with the temporal bisection task, while recording brain activity from electroencephalography (EEG). Methods: Nine adult participants were trained to distinguish between two durations of visual stimuli as either "short" (400 msec) or "long" (1600 msec). They were then presented with stimulus durations in between the long and short stimuli. EEG data from 128 electrodes were examined with a novel analytical method that identifies segments of sustained cortical activity during the task. Results: Participants tended to categorize intermediate durations as "long" more frequently than "short" and were thus experiencing time as moving faster while overestimating the amount of time passing. Their mean bisection point (during which frequency of selecting short vs. long is equal) was closer to the geometric mean of task stimuli (800 msec) rather than the arithmetic mean (1000 msec). In contrast, sustained brain activity occurred closer to the arithmetic mean. The recurrence rate of this activity was highly related to the bisection point, especially when analyzed within naturally occurring theta oscillations (4-8 Hz) (r = -0.90). Discussion: Sustained activity across the cortex within the theta range may reflect temporal durations, whereas its repeated appearance relates to the subjective feeling of time passing.

A Synthetic Ultra-Wideband Transceiver for Millimeter-Wave Imaging Applications

In this work, we present a transceiver front-end in SiGe BiCMOS technology that can provide an ultra-wide bandwidth of 100 GHz at millimeter-wave frequencies. The front-end utilizes an innovative arrangement to efficiently distribute broadband-generated pulses and coherently combine received pulses with minimal loss. This leads to the realization of a fully integrated ultra-high-resolution imaging chip for biomedical applications. We realized an ultra-wide imaging band-width of 100 GHz via the integration of two adjacent disjointed frequency sub-bands of 10-50 GHz and 50-110 GHz. The transceiver front-end is capable of both transmit (TX) and receive (RX) operations. This is a crucial component for a system that can be expanded by repeating a single unit cell in both the horizontal and vertical directions. The imaging elements were designed and fabricated in Global Foundry 130-nm SiGe 8XP process technology.

Hierarchical Classification Strategy for Mitigating the Impact of The Presence of Pain in fNIRS-based BCIs

Brain computer interfaces (BCIs) can find applications in assistive systems for patients who experience conditions that impede their motor abilities. A BCI uses signals acquired from the brain to control external devices. As physical pain influences cortical signals, the presence of pain can negatively impact the performance of the BCI. In this work, we propose a strategy to mitigate this negative impact. Cortical signals are acquired from test subjects while they performed two mental arithmetic tasks, in the presence and the absence of painful stimuli. The task of the BCI is to reliably classify the two mental arithmetic tasks from the cortical recordings, irrespective of the presence or the absence of pain. We propose to do this classification, hierarchically, in two levels. In the first level, the data is classified into those captured in the presence and the absence of pain. Depending on the results of the classification from the first level, in the second level, the BCI performs the classification of tasks using a classifier trained either in the presence or the absence of pain. A 1-dimensional convolutional neural network (1D-CNN) is used for classification at both levels. It is observed that using this hierarchical strategy, the BCI is able to classify the tasks with an accuracy greater than 90%, irrespective of the presence or the absence of pain. Given that the presence of physical pain has shown previously to reduce the classification accuracy of a BCI to almost chance levels, this mitigation strategy will be a significant step towards enhancing the performance of BCIs when they are used in assistive systems for patients.

Demographic Information Fusion Using Attentive Pooling In CNN-GRU Model For Systolic Blood Pressure Estimation

Fusing demographic information into deep learning models has become of interest in recent end-to-end cuff-less blood pressure (BP) estimation studies in order to achieve improved performance. Conventionally, the demographic feature vector is concatenated with the pooled embedding vector. Here, using an attention-based convolutional neural network-gated recurrent unit (CNN-GRU), we present a new approach and fuse the demographic information into the attentive pooling module. Our results demonstrate that, under calibration-based testing protocol, the proposed approach provides improved systolic blood pressure (SBP) estimation accuracy (with R2=0.86 and mean absolute error (MAE)=4.90 mmHg) compared to both the baseline model with no demographic information fused, and the conventional approach of fusing demographic information. Our work showcases the feasibility of using attention-based methods to combine demographic features with deep learning models, and suggests new ways for fusing demographic information in deep learning models to achieve improved BP estimation accuracy.

PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods

There has been a growing interest in developing cuff-less blood pressure (BP) estimation methods to enable continuous BP monitoring from electrocardiogram (ECG) and/or photoplethysmogram (PPG) signals. The majority of these methods have been evaluated using publicly-available datasets, however, there exist significant discrepancies across studies with respect to the size, the number of subjects, and the applied pre-processing steps for the data that is eventually used for training and testing the models. Such differences make conducting performance comparison across models largely unfair, and mask the generalization capability of various BP estimation methods. To fill this important gap, this paper presents "PulseDB," the largest cleaned dataset to date, for benchmarking BP estimation models that also fulfills the requirements of standardized testing protocols. PulseDB contains 1) 5,245,454 high-quality 10 -s segments of ECG, PPG, and arterial BP (ABP) waveforms from 5,361 subjects retrieved from the MIMIC-III waveform database matched subset and the VitalDB database; 2) subjects' identification and demographic information, that can be utilized as additional input features to improve the performance of BP estimation models, or to evaluate the generalizability of the models to data from unseen subjects; and 3) positions of the characteristic points of the ECG/PPG signals, making PulseDB directly usable for training deep learning models with minimal data pre-processing. Additionally, using this dataset, we conduct the first study to provide insights about the performance gap between calibration-based and calibration-free testing approaches for evaluating generalizability of the BP estimation models. We expect PulseDB, as a user-friendly, large, comprehensive and multi-functional dataset, to be used as a reliable source for the evaluation of cuff-less BP estimation methods.

Identifying mild traumatic brain injury using measures of frequency-specified networks

Objective. Early diagnosis of mild traumatic brain injury (mTBI) is challenging, yet crucial for providing patients with timely treatments and minimizing the risks of developing injury-related disorders. To tackle this problem, this paper presents a framework based on measures of frequency-specified brain functional networks identifying mTBI.Approach. Cortical activity of 15 control and 15 injury Thy1-GCaMP6s mice are recorded, using widefield calcium imaging, prior to and 20 minutes after inducing injury. Power spectral distribution (PSD) of the recorded cortical activities are examined, and the frequency bands with significant difference in PSD between the injury and control groups are identified. Frequency-specified functional networks are then constructed. Employing graph theoretical analysis, various network measures from the constructed frequency-specified functional networks are extracted and used as features. Several classifiers are utilized to evaluate the performance of the computed network measures, either individually or collectively as features, to classify mTBI from control.Main results. Spectral analysis reveals the presence of two dominant frequency bands (low: <1 Hz) and high: [1-8] Hz) in the cortical activities recorded via calcium imaging. Comparison of the brain networks of control and injury groups shows significant reduction (p<0.05) in global functional connectivity following injury, specially for the high frequency band network. Interestingly, graph measures of the high frequency band network provided higher classification accuracy results, compared to those computed from the low frequency band network, suggesting that mTBI network-based features are frequency dependent. Using all network measures collectively as a multi-measure feature vector and a CNN classifier, a model for identifying mTBI is developed, offering an average classification accuracy of 97.28%.Significance. Results signifies the importance of considering frequency-specific analysis in functional networks for mTBI identification, and demonstrate the possibility of using network measures for early mTBI diagnosis.

The Effects of Filtering PPG Signal on Pulse Arrival Time-Systolic Blood Pressure Correlation

Pulse arrival time (PAT), evaluated from electro-cardiogram (ECG) and photoplethysmogram (PPG) signals, has been widely used for cuff-less blood pressure (BP) estimation due to its high correlation with BP. However, the question of whether filtering the PPG signal impacts the extracted PAT values and consequently, the correlation between PAT and BP, has not been investigated before. In this paper, using data from 18 subjects, changes in the PAT values, and in the subject-specific PAT-systolic BP (SBP) correlation caused by filtering the PPG signal with variable cutoff frequencies in the range of 2 to 15 Hz are studied. For PAT extraction, three PPG characteristic points (foot, maximum slope and systolic peak) are considered. Results show that differences in the cutoff frequency can shift the PAT values and introduce a worst-case error of over 8.2 mmHg for SBP estimation, indicating that PPG signal filter settings can impact PAT-based BP estimations. Our study suggests that extracting the PAT from the maximum slope point of PPG signal filtered at 10 Hz provides the most stable correlation with SBP.

A Deep Learning Framework Based on Dynamic Channel Selection for Early Classification of Left and Right Hand Motor Imagery Tasks

Ideal brain-computer interfaces (BCIs) need to be efficient and accurate, demanding for classifiers that can work across subjects while providing high classification accu-racy results from recordings with short duration. To address this problem, we present a new deep learning framework for discriminating motor imagery (MI) tasks from electroen-cephalography (EEG) signals. The framework consists of a 1D convolutional neural network-long short-term memory (CNN-LSTM), combined with a dynamic channel selection approach based on Davies-Bouldin index (DBI). Using data from BCI competition IV-IIa data, the proposed framework reports an average classification accuracy of 70.17% and 76.18% when using only 800 ms and 1500 ms of the EEG data after the task onset, respectively. The proposed framework dynamically balances individual differences, achieves comparable or better performance compared to existing work, while using short duration of EEG.

Cuff-less Blood Pressure Estimation from Photoplethysmography via Visibility Graph and Transfer Learning

This paper presents a new solution that enables the use of transfer learning for cuff-less blood pressure (BP) monitoring via short duration of photoplethysmogram (PPG). The proposed method estimates BP with low computational budget by 1) creating images from segments of PPG via visibility graph (VG) that preserves the temporal information of the PPG waveform, 2) using pre-trained deep convolutional neural network (CNN) to extract feature vectors from VG images, and 3) solving for the weights and bias between the feature vectors and the reference BPs with ridge regression. Using the University of California Irvine (UCI) database consisting of 348 records, the proposed method achieves a best error performance of 0.008.46 mmHg for systolic blood pressure (SBP), and -0.045.36 mmHg for diastolic blood pressure (DBP), respectively, in terms of the mean error (ME) and the standard deviation (SD) of error, ranking grade B for SBP and grade A for DBP under the British Hypertension Society (BHS) protocol. Our novel data-driven method offers a computationally-efficient end-to-end solution for rapid and user-friendly cuff-less PPG-based BP estimation.

Cuff-Less Blood Pressure Estimation via Small Convolutional Neural Networks

Deep learning-based cuff-less blood pressure (BP) estimation methods have recently gained increased attention as they can provide accurate BP estimation with only one physiological signal as input. In this paper, we present a simple and effective method for cuff-less BP estimation by training a small-scale convolutional neural network (CNN), modified from LeNet-5, with images created from short segments of the photoplethysmogram (PPG) signal via visibility graph (VG). Results show that the trained modified LeNet-5 model achieves an error performance of 0.184±7.457 mmHg for the systolic BP (SBP), and 0.343±4.065 mmHg for the diastolic BP (DBP) in terms of the mean error (ME) and the standard deviation (SD) of error between the estimated and reference BP. Both the SBP and the DBP accuracy rank grade A under the British Hypertension Society (BHS) protocol, demonstrating that our proposed method is an accurate way for cuff-less BP estimation.

PulseLab: An Integrated and Expandable Toolbox for Pulse Wave Velocity-based Blood Pressure Estimation

In this paper, we introduce PulseLab, a comprehensive MATLAB toolbox that enables estimating the blood pressure (BP) from electrocardiogram (ECG) and photoplethysmogram (PPG) signals using pulse wave velocity (PWV)-based models. This universal framework consists of 6 sequential modules, covering end-to-end procedures that are needed for estimating BP from raw PPG/ECG data. These modules are "dataset formation", "signal pre-processing", "segmentation", "characteristic-points detection", "pulse transit time (PTT)/ pulse arrival time (PAT) calculation", and "model validation". The toolbox is expandable and its application programming interface (API) is built such that newly-derived PWV-BP models can be easily included. The toolbox also includes a user-friendly graphical user interface (GUI) offering visualization for step-by-step processing of physiological signals, position of characteristic points, PAT/PTT values, and the BP regression results. To the best of our knowledge, PulseLab is the first comprehensive toolbox that enables users to optimize their model by considering several factors along the process for obtaining the most accurate model for cuff-less BP estimation.

A Variational Encoder Framework for Decoding Behavior Choices from Neural Data

In this paper, using an adversarial variational encoder model, we propose a two-step data-driven approach to extract cross-subject feature representations from neural activity in order to decode subjects' behavior choices. First, various characteristics of the recorded behavior are computed and passed as features to a clustering model in order to categorize different behavior choices in each trial and create labels for the data. Then, we utilize a variational encoder to learn the latent space mappings from neural activity. An attached adversary network is used in a discriminative setting to detach the subject's individuality from the representations. Recorded cortical activity from Thy1-GCaMP6s transgenic mice during a motivational licking experiment was used in this study. Experimental results demonstrate the capabilities of the proposed method in extracting discriminative representations from neural data to decode behavior by achieving an average classification accuracy of 88.8% across subjects.

A Data-Driven Framework for Intention Prediction via Eye Movement With Applications to Assistive Systems

Fast and accurate human intention prediction can significantly advance the performance of assistive devices for patients with limited motor or communication abilities. Among available modalities, eye movement can be valuable for inferring the user's intention, as it can be tracked non-invasively. However, existing limited studies in this domain do not provide the level of accuracy required for the reliable operation of assistive systems. By taking a data-driven approach, this paper presents a new framework that utilizes the spatial and temporal patterns of eye movement along with deep learning to predict the user's intention. In the proposed framework, the spatial patterns of gaze are identified by clustering the gaze points based on their density over displayed images in order to find the regions of interest (ROIs). The temporal patterns of gaze are identified via hidden Markov models (HMMs) to find the transition sequence between ROIs. Transfer learning is utilized to identify the objects of interest in the displayed images. Finally, models are developed to predict the user's intention after completing the task as well as at early stages of the task. The proposed framework is evaluated in an experiment involving predicting intended daily-life activities. Results indicate that an average classification accuracy of 97.42% is achieved, which is considerably higher than existing gaze-based intention prediction studies.

Early classification of motor tasks using dynamic functional connectivity graphs from EEG

OBJECTIVE

Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.

APPROACH

The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification.

MAIN RESULTS

Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% using features extracted from only 500 ms of the post-stimulus data.

SIGNIFICANCE

Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.

Detecting mTBI by Learning Spatio-temporal Characteristics of Widefield Calcium Imaging Data Using Deep Learning

Early diagnosis of mild traumatic brain injury (mTBI) is of great interest to the neuroscience and medical communities. Widefield optical imaging of neuronal populations over the cerebral cortex in animals provides a unique opportunity to study injury-induced alternations in brain function. Using this technique, along with deep learning, the goal of this paper is to develop a framework for the detection of mTBI. Cortical activities in transgenic calcium reporter mice expressing GCaMP6s are obtained using widefield imaging from 8 mice before and after inducing an injury. Two deep learning models for differentiating mTBI from normal conditions are proposed. One model is based on the convolutional neural network-long short term memory (CNN-LSTM), and the second model is based on a 3D-convolutional neural network (3D-CNN). These two models offer the ability to capture features both in the spatial and temporal domains. Results for the average classification accuracy for the CNN-LSTM and the 3D-CNN are 97.24% and 91.34%, respectively. These results are notably higher than the case of using the classical machine learning methods, demonstrating the importance of utilizing both the spatial and temporal information for early detection of mTBI.

Classification of EEG Data Based on the Spatio-Temporo-Rhythmic Characteristics of the Task-Discriminating Functional Sub-networks

We propose a new approach that utilizes the dynamic state of cortical functional connectivity for the classification of task-based electroencephalographic (EEG) data. We introduce a novel feature extraction framework that locates functional networks in the cortex as they convene at different time intervals across different frequency bands. The framework starts by applying the wavelet transform to isolate, then augment, EEG frequency bands. Next, the time intervals of stationary functional states, within the augmented data, are identified using the source-informed segmentation algorithm. Functional networks are localized in the brain, during each segment, using a singular value decomposition-based approach. For feature selection, we propose a discriminative-associative algorithm, and use it to find the sub-networks showing the highest recurrence rate differences across the target tasks. The sequences of augmented functional networks are projected onto the identified sub-networks, for the final sequences of features. A dynamic recurrent neural network classifier is then used for classification. The proposed approach is applied to experimental EEG data to classify motor execution and motor imagery tasks. Our results show that an accuracy of 90% can be achieved within the first 500 msec of the cued task-planning phase.

Detection of Mild Traumatic Brain Injury via Topological Graph Embedding and 2D Convolutional Neural Networks

Early diagnosis of mild traumatic brain injury (mTBI) is challenging, yet significantly important in order to grant the patients with timely treatment and mitigating the risks of possible long-term psychiatric and neurological disorders. To tackle this problem, in this paper, we develop an mTBI detection framework based on graph embedding features combined with convolutional neural networks (CNN). Cortical activity in transgenic calcium reporter mice expressing Thy1-GCaMP6s is recorded in two sessions, prior to and after inducing injury. Functional networks are then constructed for recordings obtained in each session. The Node2vec algorithm is employed to represent nodes of these networks in the node embedding space. Node embedding feature vectors are then aligned, compressed, and represented as three-channel images. A CNN model is used for the classification of brain networks into two categories of normal and mTBI. A maximum classification accuracy of 95.4% is achieved. Our results suggest that functional networks as biomarkers along with the proposed method can effectively be used for detecting mTBI.

Quantifying Changes in Brain Function Following Injury via Network Measures

Widefield optical imaging enables longitudinal capturing of the large-scale network activity of the cerebral cortex in awake behaving animals. Utilizing this technique, we investigate how the brain's functional network structure is altered following injury. Cortical activity in transgenic calcium reporter mice expressing GCaMP6s is recorded in two sessions, prior and after inducing the injury. As a quantitative network measure, "communicability" which is indicative of how easily the information flows between nodes in a network via both direct and indirect paths, is considered. Compared to control subjects, results confirm altered functional network structure after the injury. Links that significantly contribute to the altered connectivity after the injury are spatially localized. It is shown that spectral clustering applied to the communicability networks is capable of distinguishing trials associated with pre-and post-injury sessions, suggesting that this approach can be used as an effective way to track changes in brain networks following the injury, and potentially, towards recovery.

On fractality of functional near-infrared spectroscopy signals: analysis and applications

Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Methods capable of revealing nonlinearities in fNIRS recordings can provide new insights about how the brain functions. Aim: The temporal characteristics of fNIRS signals are explored by comprehensively investigating their fractal properties. Approach: Fractality of fNIRS signals is analyzed using scaled windowed variance (SWV), as well as using visibility graph (VG), a method which converts a given time series into a graph. Additionally, the fractality of fNIRS signals obtained under resting-state and task-based conditions is compared, and the application of fractality in differentiating brain states is demonstrated for the first time via various classification approaches. Results: Results from SWV analysis show the existence of high fractality in fNIRS recordings. It is shown that differences in the temporal characteristics of fNIRS signals related to task-based and resting-state conditions can be revealed via the VGs constructed for each case. Conclusions: fNIRS recordings, regardless of the experimental conditions, exhibit high fractality. Furthermore, VG-based metrics can be employed to differentiate rest and task-execution brain states.

Investigating learning-related neural circuitry with chronic in vivo optical imaging

Fundamental aspects of brain function, including development, plasticity, learning, and memory, can take place over time scales of days to years. Chronic in vivo imaging of neural activity with cellular resolution is a powerful method for tracking the long-term activity of neural circuits. We review recent advances in our understanding of neural circuit function from diverse brain regions that have been enabled by chronic in vivo cellular imaging. Insight into the neural basis of learning and decision-making, in particular, benefit from the ability to acquire longitudinal data from genetically identified neuronal populations, deep brain areas, and subcellular structures. We propose that combining chronic imaging with further experimental and computational innovations will advance our understanding of the neural circuit mechanisms of brain function.

Eye Gaze-based Early Intent Prediction Utilizing CNN-LSTM

In assistive technologies designed for patients with extremely limited motor or communication capabilities, it is of significant importance to accurately predict the intention of the user, in a timely manner. This paper presents a new framework for the early prediction of the user's intent via their eye gaze. The seen objects in the displayed images, and the order of their selection are identified from the spatial and temporal information of the gaze. By employing a combination of convolution neuronal network (CNN) and long short term memory (LSTM), early prediction of the user's intention is enabled. The proposed framework is tested using experimental data obtained from eight subjects. Results demonstrate an average accuracy of 82.27% across all considered intended tasks for early prediction, confirming the effectiveness of the proposed method.

On the Spatiotemporal Characteristics of Class-Discriminating Functional Networks

We target the problem of identifying brain's functional networks that are discriminatory across classes of tasks, using data obtained through electroencephalography (EEG). A three-step framework is presented. First, the EEG data is segmented to identify the intervals during which cortical functional networks remain quasi-stationary. Second, these functional networks are spatially localized in the cortex. Finally, by employing the proposed discriminative Boolean matrix factorization (DBMF) algorithm, functional networks that are most recurrent in one class of tasks, but are least recurrent in the other are identified. The DBMF algorithm is capable of providing the spatial maps of the discriminative functional networks as well as information about their dynamic occurrence over time. The framework is applied to experimental EEG data, recorded during a motor task. The results show that the proposed framework identifies several parietal/motor functional networks as being the most discriminatory for motor execution trials from non-execution trials.

Source-Informed Segmentation: A Data-Driven Approach for the Temporal Segmentation of EEG

GOAL

Understanding the dynamics of brain function through non-invasive monitoring techniques requires the development of computational methods that can deal with the non-stationary properties of recorded activities. As a solution to this problem, a new data-driven segmentation method for recordings obtained through electroencephalography (EEG) is presented.

METHODS

The proposed method utilizes singular value decomposition (SVD) to identify the time intervals in the EEG recordings during which the spatial distribution of clusters of active cortical neurons remains quasi-stationary. Theoretical analysis shows that the spatial locality features of these clusters can be, asymptotically, captured by the most significant left singular subspace of the EEG data. A reference/sliding window approach is employed to dynamically extract this feature subspace, and the running projection error is monitored for significant changes using Kolmogorov-Smirnov test.

RESULTS

Simulation results, for a wide range of possible scenarios regarding the spatial distribution of active cortical neurons, show that the algorithm is successful in accurately detecting the segmental structure of the simulated EEG data. The algorithm is also applied to experimental EEG recordings of a modified visual oddball task. Results identify a unique sequence of dynamic patterns in the event-related potential (ERP) response to each of the three involved stimuli.

CONCLUSION

The proposed method, without using source localization methods or scalp topographical maps, is able to identify intervals of quasi-stationarity in the EEG recordings.

SIGNIFICANCE

The proposed segmentation technique can offer new insights on the dynamics of functional organization of the brain in action.

Decoding cortical brain states from widefield calcium imaging data using visibility graph

Widefield optical imaging of neuronal populations over large portions of the cerebral cortex in awake behaving animals provides a unique opportunity for investigating the relationship between brain function and behavior. In this paper, we demonstrate that the temporal characteristics of calcium dynamics obtained through widefield imaging can be utilized to infer the corresponding behavior. Cortical activity in transgenic calcium reporter mice (n=6) expressing GCaMP6f in neocortical pyramidal neurons is recorded during active whisking (AW) and no whisking (NW). To extract features related to the temporal characteristics of calcium recordings, a method based on visibility graph (VG) is introduced. An extensive study considering different choices of features and classifiers is conducted to find the best model capable of predicting AW and NW from calcium recordings. Our experimental results show that temporal characteristics of calcium recordings identified by the proposed method carry discriminatory information that are powerful enough for decoding behavior.

Cortical activity changes as related to oral irritation-an fNIRS study

We present the first investigation of how the cortical regions of the brain respond to the sensations related to oral irritation, using functional near-infrared spectroscopy (fNIRS). fNIRS is used to record irritant-induced activities in the prefrontal and somatosensory regions of nine healthy individuals. Two types of solutions, irritant-free and soft tissue-irritant-contained, are adopted as the stimuli for the control and task experiments, respectively. Our findings reveal that both somatosensory and prefrontal regions show activity as a result of oral irritation. Furthermore, using moving window analysis, we identify the time interval during which the largest number of channels (indicative of high involvement of cortical regions) show irritant-induced activity. Our results indicate that fNIRS can be used to study brain activities related to oral irritation.

BioMEMS-based coding for secure medical diagnostic devices

Trustworthy and usable point-of-care solutions require not only effective disease diagnostic procedures to ensure delivery of rapid and accurate outcomes, but also lightweight privacy-preserving capabilities. In this paper, we present a Biomedical Microelectromachanical System (BioMEMS)-based sensor for portable, inexpensive smartphone-based biomarker detection. The biosensor presented here provides the ability for signal encryption at the physical sensor level to ensure patient's diagnostic confidentiality. Our results show that this design allow us to protect the samples measurements while accurately distinguish different test samples.

On the invariance of EEG-based signatures of individuality with application in biometric identification

One of the main challenges in EEG-based biometric systems is to extract reliable signatures of individuality from recorded EEG data that are also invariant against time. In this paper, we investigate the invariability of features that are extracted based on the spatial distribution of the spectral power of EEG data corresponding to 2-second eyes-closed resting-state (ECRS) recording, in different scenarios. Eyes-closed resting-state EEG signals in 4 healthy adults are recorded in two different sessions with an interval of at least one week between sessions. The performance in terms of correct recognition rate (CRR) is examined when the training and testing datasets are chosen from the same recording session, and when the training and testing datasets are chosen from different sessions. It is shown that an CRR of 92% can be achieved based on the proposed features when the training and testing datasets are taken from different sessions. To reduce the number of recording channels, principal component analysis (PCA) is also employed to identify channels that carry the most discriminatory information across individuals. High CRR is obtained based on the data from channels mostly covering the occipital region. The results suggest that features based on the spatial distribution of the spectral power of the short-time (e.g. 2 seconds) ECRS recordings can have great potentials in EEG-based biometric identification systems.

Global EEG segmentation using singular value decomposition

In this paper, we propose a method based on singular value decomposition (SVD) for segmenting multichannel electroencephalography (EEG) data into temporal blocks during which the spatial distributions of the underlying active neuronal generators stay fixed. We locate segment boundaries by statistically comparing the residual error resulting from projecting the data under a reference window, on one hand, and a sliding window, on the other hand, onto a feature subspace. The basis of this subspace is the most significant left eigenvectors of the data block under the reference window. The statistical testing is performed using the Kolmogorov-Smirnov (K-S) test. To enhance the reliability of the K-S test, the consecutive K-S decisions are aggregated under a given decision window. Simulation results confirm that the proposed algorithm can successfully detect segment boundaries under a wide range of different conditions.

Evaluation of non-invasive multispectral imaging as a tool for measuring the effect of systemic therapy in Kaposi sarcoma

Diffuse multi-spectral imaging has been evaluated as a potential non-invasive marker of tumor response. Multi-spectral images of Kaposi sarcoma skin lesions were taken over the course of treatment, and blood volume and oxygenation concentration maps were obtained through principal component analysis (PCA) of the data. These images were compared with clinical and pathological responses determined by conventional means. We demonstrate that cutaneous lesions have increased blood volume concentration and that changes in this parameter are a reliable indicator of treatment efficacy, differentiating responders and non-responders. Blood volume decreased by at least 20% in all lesions that responded by clinical criteria and increased in the two lesions that did not respond clinically. Responses as assessed by multi-spectral imaging also generally correlated with overall patient clinical response assessment, were often detectable earlier in the course of therapy, and are less subject to observer variability than conventional clinical assessment. Tissue oxygenation was more variable, with lesions often showing decreased oxygenation in the center surrounded by a zone of increased oxygenation. This technique could potentially be a clinically useful supplement to existing response assessment in KS, providing an early, quantitative, and non-invasive marker of treatment effect.

Accurate optical parameter extraction procedure for broadband near-infrared spectroscopy of brain matter

Modeling behavior of broadband (30 to 1000 MHz) frequency modulated near-infrared (NIR) photons through a phantom is the basis for accurate extraction of optical absorption and scattering parameters of biological turbid media. Photon dynamics in a phantom are predicted using both analytical and numerical simulation and are related to the measured insertion loss (IL) and insertion phase (IP) for a given geometry based on phantom optical parameters. Accuracy of the extracted optical parameters using finite element method (FEM) simulation is compared to baseline analytical calculations from the diffusion equation (DE) for homogenous brain phantoms. NIR spectroscopy is performed using custom-designed, broadband, free-space optical transmitter (Tx) and receiver (Rx) modules that are developed for photon migration at wavelengths of 680, 780, and 820 nm. Differential detection between two optical Rx locations separated by 0.3 cm is employed to eliminate systemic artifacts associated with interfaces of the optical Tx and Rx with the phantoms. Optical parameter extraction is achieved for four solid phantom samples using the least-square-error method in MATLAB (for DE) and COMSOL (for FEM) simulation by fitting data to measured results over broadband and narrowband frequency modulation. Confidence in numerical modeling of the photonic behavior using FEM has been established here by comparing the transmission mode's experimental results with the predictions made by DE and FEM for known commercial solid brain phantoms.

Biophotonics techniques for structural and functional imaging, in vivo

In vivo optical imaging is being conducted in a variety of medical applications, including optical breast cancer imaging, functional brain imaging, endoscopy, exercise medicine, and monitoring the photodynamic therapy and progress of neoadjuvant chemotherapy. In the past three decades, in vivo diffuse optical breast cancer imaging has shown promising results in cancer detection, and monitoring the progress of neoadjuvant chemotherapy. The use of near infrared spectroscopy for functional brain imaging has been growing rapidly. In fluorescence imaging, the difference between autofluorescence of cancer lesions compared to normal tissues were used in endoscopy to distinguish malignant lesions from normal tissue or inflammation and in determining the boarders of cancer lesions in surgery. Recent advances in drugs targeting specific tumor receptors, such as monoclonal antibodies (mAb), has created a new demand for developing non-invasive in vivo imaging techniques for detection of cancer biomarkers, and for monitoring their down regulations during therapy. Targeted treatments, combined with new imaging techniques, are expected to potentially result in new imaging and treatment paradigms in cancer therapy. Similar approaches can potentially be applied for the characterization of other disease-related biomarkers. In this chapter, we provide a review of diffuse optical and fluorescence imaging techniques with their application in functional brain imaging and cancer diagnosis.

Biophotonics techniques for structural and functional imaging, in vivo

In vivo optical imaging is being conducted in a variety of medical applications, including optical breast cancer imaging, functional brain imaging, endoscopy, exercise medicine, and monitoring the photodynamic therapy and progress of neoadjuvant chemotherapy. In the past three decades, in vivo diffuse optical breast cancer imaging has shown promising results in cancer detection, and monitoring the progress of neoadjuvant chemotherapy. The use of near infrared spectroscopy for functional brain imaging has been growing rapidly. In fluorescence imaging, the difference between autofluorescence of cancer lesions compared to normal tissues were used in endoscopy to distinguish malignant lesions from normal tissue or inflammation and in determining the boarders of cancer lesions in surgery. Recent advances in drugs targeting specific tumor receptors, such as AntiBodies (MAB), has created a new demand for developing non-invasive in vivo imaging techniques for detection of cancer biomarkers, and for monitoring their down regulations during therapy. Targeted treatments, combined with new imaging techniques, are expected to potentially result in new imaging and treatment paradigms in cancer therapy. Similar approaches can potentially be applied for the characterization of other disease-related biomarkers. In this chapter, we provide a review of diffuse optical and fluorescence imaging techniques with their application in functional brain imaging and cancer diagnosis.

Normative database of judgment of complexity task with functional near infrared spectroscopy--application for TBI

The ability to assess frontal lobe function in a rapid, objective, and standardized way, without the need for expertise in cognitive test administration might be particularly helpful in mild traumatic brain injury (TBI), where objective measures are needed. Functional near infrared spectroscopy (fNIRS) is a reliable technique to noninvasively measure local hemodynamic changes in brain areas near the head surface. In this paper, we are combining fNIRS and frameless stereotaxy which allowed us to co-register the functional images with previously acquired anatomical MRI volumes. In our experiment, the subjects were asked to perform a task, evaluating the complexity of daily life activities, previously shown with fMRI to activate areas of the anterior frontal cortex. We reconstructed averaged oxyhemoglobin and deoxyhemoglobin data from 20 healthy subjects in a spherical coordinate. The spherical coordinate is a natural representation of surface brain activation projection. Our results show surface activation projected from the medial frontopolar cortex which is consistent with previous fMRI results. With this original technique, we will construct a normative database for a simple cognitive test which can be useful in evaluating cognitive disability such as mild traumatic brain injury.

A hematoma detector-a practical application of instrumental motion as signal in near infra-red imaging

In this paper we discuss results based on using instrumental motion as a signal rather than treating it as noise in Near Infra-Red (NIR) imaging. As a practical application to demonstrate this approach we show the design of a novel NIR hematoma detection device. The proposed device is based on a simplified single source configuration with a dual separation detector array and uses motion as a signal for detecting changes in blood volume in the dural regions of the head. The rapid triage of hematomas in the emergency room will lead to improved use of more sophisticated/expensive imaging facilities such as CT/MRI units. We present simulation results demonstrating the viability of such a device and initial phantom results from a proof of principle device. The results demonstrate excellent localization of inclusions as well as good quantitative comparisons.

Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors

We describe a novel reconstruction algorithm based on Principal Component Analysis (PCA) applied to multi-spectral imaging data. Using numerical phantoms, based on a two layered skin model developed previously, we found analytical expressions, which convert qualitative PCA results into quantitative blood volume and oxygenation values, assuming the epidermal thickness to be known. We also evaluate the limits of accuracy of this method when the value of the epidermal thickness is not known. We show that blood volume can reliably be extracted (less than 6% error) even if the assumed thickness deviates 0.04mm from the actual value, whereas the error in blood oxygenation can be as large as 25% for the same deviation in thickness. This PCA based reconstruction was found to extract blood volume and blood oxygenation with less than 8% error, if the underlying structure is known. We then apply the method to in vivo multi-spectral images from a healthy volunteer's lower forearm, complemented by images of the same area using Optical Coherence Tomography (OCT) for measuring the epidermal thickness. Reconstruction of the imaging results using a two layered analytical skin model was compared to PCA based reconstruction results. A point wise correlation was found, showing the proof of principle of using PCA based reconstruction for blood volume and oxygenation extraction.