Background: For many years, clinicians have been seeking for objective pain assessment solutions via neuroimaging techniques, focusing on the brain to detect human pain. Unfortunately, most of those techniques are not applicable in the clinical environment or lack accuracy. Objective: This study aimed to test the feasibility of a mobile neuroimaging-based clinical augmented reality (AR) and artificial intelligence (AI) framework, CLARAi, for objective pain detection and also localization direct from the patient's brain in real time. Methods: Clinical dental pain was triggered in 21 patients by hypersensitive tooth stimulation with 20 consecutive descending cold stimulations (32°C-0°C). We used a portable optical neuroimaging technology, functional near-infrared spectroscopy, to gauge their cortical activity during evoked acute clinical pain. The data were decoded using a neural network (NN)–based AI algorithm to classify hemodynamic response data into pain and no-pain brain states in real time. We tested the performance of several networks (NN with 7 layers, 6 layers, 5 layers, 3 layers, recurrent NN, and long short-term memory network) upon reorganized data features on pain diction and localization in a simulated real-time environment. In addition, we also tested the feasibility of transmitting the neuroimaging data to an AR device, HoloLens, in the same simulated environment, allowing visualization of the ongoing cortical activity on a 3-dimensional brain template virtually plotted on the patients' head during clinical consult. Results: The artificial neutral network (3-layer NN) achieved an optimal classification accuracy at 80.37% (126,000/156,680) for pain and no pain discrimination, with positive likelihood ratio (PLR) at 2.35. We further explored a 3-class localization task of left/right side pain and no-pain states, and convolutional NN-6 (6-layer NN) achieved highest classification accuracy at 74.23% (1040/1401) with PLR at 2.02. Conclusions: Additional studies are needed to optimize and validate our prototype CLARAi framework for other pains and neurologic disorders. However, we presented an innovative and feasible neuroimaging-based AR/AI concept that can potentially transform the human brain into an objective target to visualize and precisely measure and localize pain in real time where it is most needed: in the doctor's office. International Registered Report Identifier (IRRID): RR1-10.2196/13594
Abstract Background Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been developed to measure the changes of cerebral blood oxygenation associated with brain activities. To date, for functional brain mapping applications, there is no standard on-line method for analysing NIRS data. Methods In this paper, a novel on-line NIRS data analysis framework taking advantages of both the general linear model (GLM) and the Kalman estimator is devised. The Kalman estimator is used to update the GLM coefficients recursively, and one critical coefficient regarding brain activities is then passed to a t -statistical test. The t -statistical test result is used to update a topographic brain activation map. Meanwhile, a set of high-pass filters is plugged into the GLM to prevent very low-frequency noises, and an autoregressive (AR) model is used to prevent the temporal correlation caused by physiological noises in NIRS time series. A set of data recorded in finger tapping experiments is studied using the proposed framework. Results The obtained results suggest that the method can effectively track the task related brain activation areas, and prevent the noise distortion in the estimation while the experiment is running. Thereby, the potential of the proposed method for real-time NIRS-based brain imaging was demonstrated. Conclusions This paper presents a novel on-line approach for analysing NIRS data for functional brain mapping applications. This approach demonstrates the potential of a real-time-updating topographic brain activation map.
Pain is a complex experience that involves sensation, emotion, and cognition. The subjectivity of the traditional pain measurement tools has expedited the interest in developing neuroimaging techniques to monitor pain objectively. Among noninvasive neuroimaging techniques, functional near-infrared spectroscopy (fNIRS) has balanced spatial and temporal resolution; yet, it is portable, quiet, and cost-effective. These features enable fNIRS to image the cortical mechanisms of pain in a clinical environment. In this article, we evaluated pain neuroimaging studies that used the fNIRS technique in the past decade. Starting from the experimental design, we reviewed the regions of interest, probe localization, data processing, and primary findings of these existing fNIRS studies. We also discussed the fNIRS imaging's potential as a brain surveillance technique for pain, in combination with artificial intelligence and extended reality techniques. We concluded that fNIRS is a brain imaging technique with great potential for objective pain assessment in the clinical environment.
Functional near-infrared spectroscopy (fNIRS) is an emerging brain imaging technique. Recent fNIRS studies confirm that the intrinsic fluctuation can be robustly detected by functional near infrared spectroscopy (fNIRS), the phenomenon is also termed as resting state functional connectivity (RSFC). However, functional connectivity exists not only during the resting state. Therefore, one important question is that whether the functional connectivity patterns during both states are consistent. In this paper, we investigate the functional connectivity during both resting state and task state. The comparison result suggests that the functional connectivity patterns revealed by fNIRS signal during both states are similar.
This study used an emerging brain imaging technique, functional near-infrared spectroscopy (fNIRS), to investigate functional brain activation and connectivity that modulates sometimes traumatic pain experience in a clinical setting. Hemodynamic responses were recorded at bilateral somatosensory (S1) and prefrontal cortices (PFCs) from 12 patients with dentin hypersensitivity in a dental chair before, during, and after clinical pain. Clinical dental pain was triggered with 20 consecutive descending cold stimulations (32° to 0°C) to the affected teeth. We used a partial least squares path modeling framework to link patients’ clinical pain experience with recorded hemodynamic responses at sequential stages and baseline resting-state functional connectivity (RSFC). Hemodynamic responses at PFC/S1 were sequentially elicited by expectation, cold detection, and pain perception at a high-level coefficient (coefficients: 0.92, 0.98, and 0.99, P < 0.05). We found that the pain ratings were positively affected only at a moderate level of coefficients by such sequence of functional activation (coefficient: 0.52, P < 0.05) and the baseline PFC-S1 RSFC (coefficient: 0.59, P < 0.05). Furthermore, when the dental pain had finally subsided, the PFC increased its functional connection with the affected S1 orofacial region contralateral to the pain stimulus and, in contrast, decreased with the ipsilateral homuncular S1 regions ( P < 0.05). Our study indicated for the first time that patients’ clinical pain experience in the dental chair can be predicted concomitantly by their baseline functional connectivity between S1 and PFC, as well as their sequence of ongoing hemodynamic responses. In addition, this linked cascade of events had immediate after-effects on the patients’ brain connectivity, even when clinical pain had already ceased. Our findings offer a better understating of the ongoing impact of affective and sensory experience in the brain before, during, and after clinical dental pain.
