Can you smell pain?: A Multi-modal signature for chronic pain
Michal Weiss#, Hadeel Salameh, Elias Mansour, Hossam Haick and Pavel Goldstein - School of Public Health
Machine Learning
Deep learning
Neural Networks
Health
PhD Grant 2022
Chronic pain is mainly a nervous system problem. In Fibromyalgia, a widespread pain syndrome, the pain is accompanied with impact on other body systems, like autonomous nervous system, metabolic processes and more. It affects mostly women with the prevalence of about 7 %. It is not clear what is the root cause of pain in Fibromyalgia, and it takes years to diagnose it, as there is no specific test for it.
In this study, we collaborate with the Technion to utilize a new modality for non-invasive Fibromyalgia diagnosis, by analyzing the pattern of biochemical compounds that are naturally exerted from the body via sweating and exhalation. These substances, that are collected non-invasively, are indicative of other process in the body, that might be altered in Fibromyalgia. We plan to build a sensor (electronic nose) that can detect or “smell” a specific volatile organic compounds pattern in Fibromyalgia. In addition, we will combine other measures to create a multi-modal non-invasive Fibromyalgia signature, covering metabolic process, autonomous nervous system activity, and brain activity. We assess autonomous nervous system activity by monitoring heart rate and sweat rate. Additionally, we record hemodynamic brain responses that are related to brain activity with a mobile fnirs (functional near infra-red spectroscopy) device.
Fibromyalgia patients are known to have an altered response to stress. It is also known that pain and stress have mutual interactions, but the mechanism is not clear. In our study we record the multi-modal measures during rest and during stress, in order to shed more light on stress-pain interactions.
Our study is currently running and is planned to include 46 patients with Fibromyalgia and 46 healthy matched controls.
We will utilize machine learning algorithms to find a set of biochemical and neurophysiological patterns associated with Fibromyalgia, that could be translated in the future to a diagnostic tool.
Researchers affiliations:
- Integrative Pain Laboratory (iPainLab), School of Public health, University of Haifa, Israel
- Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel
This research is supported by a PhD grant from the Data Science Research Center (DSRC) at the University of Haifa, Israel.
For more info on our research please visit our lab website: https://pain.haifa.ac.il
Chronic pain is mainly a nervous system problem. In Fibromyalgia, a widespread pain syndrome, the pain is accompanied with impact on other body systems, like autonomous nervous system, metabolic processes and more. It affects mostly women with the prevalence of about 7 %. It is not clear what is the root cause of pain in Fibromyalgia, and it takes years to diagnose it, as there is no specific test for it.
In this study, we collaborate with the Technion to utilize a new modality for non-invasive Fibromyalgia diagnosis, by analyzing the pattern of biochemical compounds that are naturally exerted from the body via sweating and exhalation. These substances, that are collected non-invasively, are indicative of other process in the body, that might be altered in Fibromyalgia. We plan to build a sensor (electronic nose) that can detect or “smell” a specific volatile organic compounds pattern in Fibromyalgia. In addition, we will combine other measures to create a multi-modal non-invasive Fibromyalgia signature, covering metabolic process, autonomous nervous system activity, and brain activity. We assess autonomous nervous system activity by monitoring heart rate and sweat rate. Additionally, we record hemodynamic brain responses that are related to brain activity with a mobile fnirs (functional near infra-red spectroscopy) device.
Fibromyalgia patients are known to have an altered response to stress. It is also known that pain and stress have mutual interactions, but the mechanism is not clear. In our study we record the multi-modal measures during rest and during stress, in order to shed more light on stress-pain interactions.
Our study is currently running and is planned to include 46 patients with Fibromyalgia and 46 healthy matched controls.
We will utilize machine learning algorithms to find a set of biochemical and neurophysiological patterns associated with Fibromyalgia, that could be translated in the future to a diagnostic tool.
Researchers affiliations:
- Integrative Pain Laboratory (iPainLab), School of Public health, University of Haifa, Israel
- Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel
This research is supported by a PhD grant from the Data Science Research Center (DSRC) at the University of Haifa, Israel.
For more info on our research please visit our lab website: https://pain.haifa.ac.il