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.

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.

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.

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.