SignificanceThe accurate assessment and classification of residual consciousness are crucial for optimizing therapeutic interventions in patients with disorders of consciousness (DOCs). However, there remains an absence of effective and definitive diagnostic methods for DOC in clinical practice.AimThe primary objective was to investigate the feasibility of utilizing resting state functional near-infrared spectroscopy (rs-fNIRS) for evaluating residual consciousness. The secondary objective was to explore the distinguishing characteristics that are more effective in differentiating between the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) and to identify the machine learning model that offers superior classification accuracy.ApproachWe utilized rs-fNIRS to evaluate the residual consciousness in patients with DOC. Specifically, rs-fNIRS was used to construct functional brain networks, and graph theory analysis was conducted to quantify the topological differences within these brain networks between MCS and UWS. After that, two classifiers were used to distinguish MCS from UWS.ResultsThe graph theory results showed that the MCS group (n=8) exhibited significantly higher global efficiency (Eg) and smaller characteristic path length (Lp) than the UWS group (n=10). The functional connectivity results showed that the correlation within the left occipital cortex (L_OC) was significantly lower in the MCS group than in the UWS group. By using the indicators with significant differences as features for further classification, the accuracy for K-nearest neighbors and linear discriminant analysis classifiers was improved by 0.89 and 0.83, respectively.ConclusionsThe resting state functional connectivity and graph theory analysis based on fNIRS has the potential to enhance the classification accuracy, providing valuable insights into the diagnosis of patients with DOC.
SignificanceAccurate evaluation of consciousness in patients with prolonged disorders of consciousness (DOC) is critical for designing therapeutic plans, determining rehabilitative services, and predicting prognosis. Effective ways for detecting consciousness in patients with DOC are still needed.AimEvaluation of the residual awareness in patients with DOC and investigation of the spatiotemporal differences in the hemodynamic responses between the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS) groups using active command-driven motor imagery (MI) tasks.ApproachIn this study, functional near-infrared spectroscopy (fNIRS) was used to measure the changes of hemodynamic responses in 19 patients with DOC (9 MCS and 10 UWS) using active command-driven MI tasks. The characteristics of the hemodynamic responses were extracted to compare the differences between the MCS and UWS groups. Moreover, the correlations between the hemodynamic responses and the clinical behavioral evaluations were also studied.ResultsThe results showed significant differences in the spatiotemporal distribution of the hemodynamic responses between the MCS and UWS groups. For the patients with MCS, significant increases in task-evoked hemodynamic responses occurred during the “YES” questions of the command-driven MI tasks. Importantly, these changes were significantly correlated with their coma-recovery scale-revised (CRS-R) scores. However, for the patients with UWS, no significant changes of the hemodynamic responses were found. Additionally, the results did not show any statistical correlation between the hemodynamic responses and their CRS-R scores.ConclusionsThe fNIRS-based command-driven MI tasks can be used as a promising tool for detecting residual awareness in patients with DOC. We hope that the findings and the active paradigm used in this study will provide useful insights into the diagnosis, therapy, and prognosis of this challenging patient population.
Digital speckle pattern interferometry (DSPI) is a full-field optical testing technique that can be used to measure tiny deformations and strains. It has been widely used in aerospace, precision manufacturing and other fields. However, the lack of effective calibration method has prevented the wider adoption of this technique. In the measurement process of DSPI, there are phase shift errors, phase noise, phase map processing algorithm errors, geometric sensitivity factors miscalibration, etc., which will lead to the final measurement error. Item-by-item calibration of the aforementioned error sources faces many difficulties in implementation and does not work well. Comprehensive calibration would be a better solution to minimize the measurement error but it is hard to perform due to the lack of suitable references for deformation measurement. In this paper, a comprehensive calibration method based on the theory of three-axis angle motions measurement using DSPI has been proposed. The tiny three-axis angle motions are loaded by Piezoelectric actuators and measured using a DSPI device based on the DSPI three-axis angle motions measurement theory. A multi-axis interferometry is used to measure the three-axis angle motions simultaneously and its output is used as the measurement reference. Because the angle motions of a rigid body instead of the deformations of an elastic body are measured, the measurement reference is readily available, yielding the successful precision calibration of the DSPI.
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