Classifying migraine using PET compressive big data analytics of brain’s μ-opioid and D2/D3 dopamine neurotransmission
Introduction: Migraine is a common and debilitating pain disorder associated with dysfunction of the central nervous system. Advanced magnetic resonance imaging (MRI) studies have reported relevant pathophysiologic states in migraine. However, its molecular mechanistic processes are still poorly und...
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2023.1173596/full |
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| author | Simeone Marino Simeone Marino Hassan Jassar Hassan Jassar Dajung J. Kim Dajung J. Kim Manyoel Lim Manyoel Lim Thiago D. Nascimento Thiago D. Nascimento Ivo D. Dinov Ivo D. Dinov Ivo D. Dinov Robert A. Koeppe Alexandre F. DaSilva Alexandre F. DaSilva |
| author_facet | Simeone Marino Simeone Marino Hassan Jassar Hassan Jassar Dajung J. Kim Dajung J. Kim Manyoel Lim Manyoel Lim Thiago D. Nascimento Thiago D. Nascimento Ivo D. Dinov Ivo D. Dinov Ivo D. Dinov Robert A. Koeppe Alexandre F. DaSilva Alexandre F. DaSilva |
| author_sort | Simeone Marino |
| collection | DOAJ |
| description | Introduction: Migraine is a common and debilitating pain disorder associated with dysfunction of the central nervous system. Advanced magnetic resonance imaging (MRI) studies have reported relevant pathophysiologic states in migraine. However, its molecular mechanistic processes are still poorly understood in vivo. This study examined migraine patients with a novel machine learning (ML) method based on their central μ-opioid and dopamine D2/D3 profiles, the most critical neurotransmitters in the brain for pain perception and its cognitive-motivational interface.Methods: We employed compressive Big Data Analytics (CBDA) to identify migraineurs and healthy controls (HC) in a large positron emission tomography (PET) dataset. 198 PET volumes were obtained from 38 migraineurs and 23 HC during rest and thermal pain challenge. 61 subjects were scanned with the selective μ-opioid receptor (μOR) radiotracer [11C]Carfentanil, and 22 with the selective dopamine D2/D3 receptor (DOR) radiotracer [11C]Raclopride. PET scans were recast into a 1D array of 510,340 voxels with spatial and intensity filtering of non-displaceable binding potential (BPND), representing the receptor availability level. We then performed data reduction and CBDA to power rank the predictive brain voxels.Results: CBDA classified migraineurs from HC with accuracy, sensitivity, and specificity above 90% for whole-brain and region-of-interest (ROI) analyses. The most predictive ROIs for μOR were the insula (anterior), thalamus (pulvinar, medial-dorsal, and ventral lateral/posterior nuclei), and the putamen. The latter, putamen (anterior), was also the most predictive for migraine regarding DOR D2/D3 BPND levels.Discussion: CBDA of endogenous μ-opioid and D2/D3 dopamine dysfunctions in the brain can accurately identify a migraine patient based on their receptor availability across key sensory, motor, and motivational processing regions. Our ML-based findings in the migraineur’s brain neurotransmission partly explain the severe impact of migraine suffering and associated neuropsychiatric comorbidities. |
| first_indexed | 2024-03-13T05:57:10Z |
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| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-03-13T05:57:10Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Pharmacology |
| spelling | doaj.art-a585b3b7f5334f49b797a91b489ad7fa2023-06-13T04:40:54ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122023-06-011410.3389/fphar.2023.11735961173596Classifying migraine using PET compressive big data analytics of brain’s μ-opioid and D2/D3 dopamine neurotransmissionSimeone Marino0Simeone Marino1Hassan Jassar2Hassan Jassar3Dajung J. Kim4Dajung J. Kim5Manyoel Lim6Manyoel Lim7Thiago D. Nascimento8Thiago D. Nascimento9Ivo D. Dinov10Ivo D. Dinov11Ivo D. Dinov12Robert A. Koeppe13Alexandre F. DaSilva14Alexandre F. DaSilva15Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United StatesDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, United StatesThe Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United StatesHeadache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United StatesThe Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United StatesHeadache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United StatesThe Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United StatesHeadache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United StatesThe Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United StatesHeadache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United StatesStatistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United StatesDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United StatesMichigan Institute for Data Science, University of Michigan, Ann Arbor, MI, United StatesDepartment of Radiology, Division of Nuclear Medicine, University of Michigan Medical School, Ann Arbor, MI, United StatesThe Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United StatesHeadache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United StatesIntroduction: Migraine is a common and debilitating pain disorder associated with dysfunction of the central nervous system. Advanced magnetic resonance imaging (MRI) studies have reported relevant pathophysiologic states in migraine. However, its molecular mechanistic processes are still poorly understood in vivo. This study examined migraine patients with a novel machine learning (ML) method based on their central μ-opioid and dopamine D2/D3 profiles, the most critical neurotransmitters in the brain for pain perception and its cognitive-motivational interface.Methods: We employed compressive Big Data Analytics (CBDA) to identify migraineurs and healthy controls (HC) in a large positron emission tomography (PET) dataset. 198 PET volumes were obtained from 38 migraineurs and 23 HC during rest and thermal pain challenge. 61 subjects were scanned with the selective μ-opioid receptor (μOR) radiotracer [11C]Carfentanil, and 22 with the selective dopamine D2/D3 receptor (DOR) radiotracer [11C]Raclopride. PET scans were recast into a 1D array of 510,340 voxels with spatial and intensity filtering of non-displaceable binding potential (BPND), representing the receptor availability level. We then performed data reduction and CBDA to power rank the predictive brain voxels.Results: CBDA classified migraineurs from HC with accuracy, sensitivity, and specificity above 90% for whole-brain and region-of-interest (ROI) analyses. The most predictive ROIs for μOR were the insula (anterior), thalamus (pulvinar, medial-dorsal, and ventral lateral/posterior nuclei), and the putamen. The latter, putamen (anterior), was also the most predictive for migraine regarding DOR D2/D3 BPND levels.Discussion: CBDA of endogenous μ-opioid and D2/D3 dopamine dysfunctions in the brain can accurately identify a migraine patient based on their receptor availability across key sensory, motor, and motivational processing regions. Our ML-based findings in the migraineur’s brain neurotransmission partly explain the severe impact of migraine suffering and associated neuropsychiatric comorbidities.https://www.frontiersin.org/articles/10.3389/fphar.2023.1173596/fullmigraine diseaseartificial intelligencedopamine (raclopride)μ-opioid (carfentanil)computer-aided diagnosisPET imaging data |
| spellingShingle | Simeone Marino Simeone Marino Hassan Jassar Hassan Jassar Dajung J. Kim Dajung J. Kim Manyoel Lim Manyoel Lim Thiago D. Nascimento Thiago D. Nascimento Ivo D. Dinov Ivo D. Dinov Ivo D. Dinov Robert A. Koeppe Alexandre F. DaSilva Alexandre F. DaSilva Classifying migraine using PET compressive big data analytics of brain’s μ-opioid and D2/D3 dopamine neurotransmission Frontiers in Pharmacology migraine disease artificial intelligence dopamine (raclopride) μ-opioid (carfentanil) computer-aided diagnosis PET imaging data |
| title | Classifying migraine using PET compressive big data analytics of brain’s μ-opioid and D2/D3 dopamine neurotransmission |
| title_full | Classifying migraine using PET compressive big data analytics of brain’s μ-opioid and D2/D3 dopamine neurotransmission |
| title_fullStr | Classifying migraine using PET compressive big data analytics of brain’s μ-opioid and D2/D3 dopamine neurotransmission |
| title_full_unstemmed | Classifying migraine using PET compressive big data analytics of brain’s μ-opioid and D2/D3 dopamine neurotransmission |
| title_short | Classifying migraine using PET compressive big data analytics of brain’s μ-opioid and D2/D3 dopamine neurotransmission |
| title_sort | classifying migraine using pet compressive big data analytics of brain s μ opioid and d2 d3 dopamine neurotransmission |
| topic | migraine disease artificial intelligence dopamine (raclopride) μ-opioid (carfentanil) computer-aided diagnosis PET imaging data |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2023.1173596/full |
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