Imaging the neural substrate of trigeminal neuralgia pain using deep learning
Copyright © 2023 Liang, Zhao, Hu, Bo, Meyyappan, Neubert and Ding..
Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were 6 regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex (dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions are taken to represent signature centers of TN pain and provide target areas for future studies seeking to understand the central mechanisms of TN.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
Frontiers in human neuroscience - 17(2023) vom: 05., Seite 1144159 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liang, Yun [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 07.06.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fnhum.2023.1144159 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2023 Liang, Zhao, Hu, Bo, Meyyappan, Neubert and Ding. | ||
| 520 | |a Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were 6 regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex (dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions are taken to represent signature centers of TN pain and provide target areas for future studies seeking to understand the central mechanisms of TN | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a convolutional neural network (CNN) | |
| 650 | 4 | |a deep learning-artificial neural network (DL-ANN) | |
| 650 | 4 | |a functional magnetic resonance imaging (fMRI) | |
| 650 | 4 | |a graph convolution neural network (GCNN) | |
| 650 | 4 | |a trigeminal neuralgia (TN) | |
| 700 | 1 | |a Zhao, Qing |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Zhenhong |e verfasserin |4 aut | |
| 700 | 1 | |a Bo, Ke |e verfasserin |4 aut | |
| 700 | 1 | |a Meyyappan, Sreenivasan |e verfasserin |4 aut | |
| 700 | 1 | |a Neubert, John K |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Mingzhou |e verfasserin |4 aut | |
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