Classifying migraine using PET compressive big data analytics of brain's μ-opioid and D2/D3 dopamine neurotransmission

Simeone Marino; Hassan Jassar; Dajung J Kim; Manyoel Lim; Thiago D Nascimento; Ivo D Dinov; Robert A Koeppe; Alexandre F DaSilva

doi:10.3389/fphar.2023.1173596

Classifying migraine using PET compressive big data analytics of brain's μ-opioid and D2/D3 dopamine neurotransmission

Front Pharmacol. 2023 Jun 13:14:1173596. doi: 10.3389/fphar.2023.1173596. eCollection 2023.

Authors

Simeone Marino^{1

2}, Hassan Jassar^{3

4}, Dajung J Kim^{3

4}, Manyoel Lim^{3

4}, Thiago D Nascimento^{3

4}, Ivo D Dinov^{1

5

6}, Robert A Koeppe⁷, Alexandre F DaSilva^{3

4}

Affiliations

¹ Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States.
² Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, United States.
³ The Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United States.
⁴ Headache 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 States.
⁵ Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
⁶ Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, United States.
⁷ Department of Radiology, Division of Nuclear Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.

Abstract

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 (BP_ND), 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 BP_ND 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.

Keywords: CBDA; PET imaging data; artificial intelligence; computer-aided diagnosis; dopamine (raclopride); migraine disease; μ-opioid (carfentanil).

Grants and funding

R01 NS094413/NS/NINDS NIH HHS/United States