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Preliminary structural MRI based brain classification of chronic pelvic pain: A MAPP network study.
Epifanio Bagarinao1, Kevin A Johnson, Katherine T Martucci
1Department of Anesthesiology, Perioperative and Pain Medicine, Division of Pain Medicine, Stanford University Medical Center, Stanford, CA, USA Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, USA Department of Physiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress, Pain and Interoception Network (PAIN), David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Department of Radiology, University of Alabama, Birmingham Medical Center, Birmingham, AL, USA Department of Anesthesiology, University of Alabama, Birmingham Medical Center, Birmingham, AL, USA.
Researchers identified brain structure patterns that can predict chronic pelvic pain (CPP) with 73% accuracy. This discovery offers a potential new biomarker for diagnosing CPP by analyzing gray matter density changes in specific brain regions.
Area of Science:
- Neuroimaging
- Neuroscience
- Medical Biomarkers
Background:
- Chronic pain conditions are often associated with alterations in brain morphology.
- Sensitive and specific brain biomarkers for chronic pelvic pain (CPP) remain underexplored.
- Previous research suggests links between visceral pain and brain structure changes.
Purpose of the Study:
- To identify brain morphology changes associated with CPP.
- To develop a classifier distinguishing CPP patients from healthy controls using neuroimaging data.
- To investigate potential brain structural biomarkers for CPP.
Main Methods:
- Utilized data from the Trans-MAPP Research Network.
- Employed a multivariate pattern classification approach.
- Applied a linear support vector machine (SVM) algorithm to gray matter images for group differentiation.
Main Results:
- A preliminary classifier achieved 73% accuracy in distinguishing individuals with CPP from controls.
- Key brain regions driving classification included the primary somatosensory cortex, pre-supplementary motor area, hippocampus, and amygdala.
- Findings suggest increased gray matter density in these regions may characterize CPP.
Conclusions:
- A preliminary brain structure-based classifier shows predictive power for CPP.
- Identified brain regions may serve as potential structural biomarkers for CPP.
- Further research is needed to refine the classifier and determine the specificity of these brain changes to CPP.
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