Preliminary structural MRI based brain classification of chronic pelvic pain: A MAPP network study

Bibliometric analysis of functional magnetic resonance imaging studies on chronic pain over the past 20 years

PURPOSE

The utilization of functional magnetic resonance imaging (fMRI) in studying the mechanisms and treatment of chronic pain has gained significant popularity. However, there is currently a dearth of literature conducting bibliometric analysis on fMRI studies focused on chronic pain.

METHODS

All the literature included in this study was obtained from the Science Citation Index Expanded of Web of Science Core Collection. We used CiteSpace and VOSviewer to analyze publications, authors, countries or regions, institutions, journals, references and keywords. Additionally, we evaluated the timeline and burst analysis of keywords, as well as the timeline and burst analysis of references. The search was conducted from 2004 to 2023 and completed within a single day on October 4th, 2023.

RESULTS

A total of 1,327 articles were retrieved. The annual publication shows an overall increasing trend. The United States has the highest number of publications and the main contributing institution is Harvard University. The journal PAIN produces the most articles. In recent years, resting-state fMRI, the prefrontal cortex, nucleus accumbens, thalamus, and migraines have been researched hotspots of fMRI studies on chronic pain.

CONCLUSIONS

This study provides an in-depth perspective on fMRI for chronic pain research, revealing key points, research hotspots and research trends, which offers valuable ideas for future research activities. It concludes with a summary of advances in clinical practice in this area, pointing out the need for critical evaluation of these findings in the light of guidelines and expert recommendations. It is anticipated that further high-quality research outputs will be generated in the future, which will facilitate the utilization of fMRI in clinical decision-making for chronic pain.

This pain drives me crazy: Psychiatric symptoms in women with interstitial cystitis/bladder pain syndrome

BACKGROUND

Interstitial cystitis/bladder pain syndrome (IC/BPS) is an at least 6-mo noninfectious bladder inflammation of unknown origin characterized by chronic suprapubic, abdominal, and/or pelvic pain. Although the term cystitis suggests an inflammatory or infectious origin, no definite cause has been identified. It occurs in both sexes, but women are twice as much affected.

AIM

To systematically review evidence of psychiatric/psychological changes in persons with IC/BPS.

METHODS

Hypothesizing that particular psychological characteristics could underpin IC/BPS, we investigated in three databases the presence of psychiatric symptoms and/or disorders and/or psychological characteristics in patients with IC/BPS using the following strategy: ("interstitial cystitis" OR "bladder pain syndrome") AND ("mood disorder" OR depressive OR antidepressant OR depression OR depressed OR hyperthymic OR mania OR manic OR rapid cyclasterisk OR dysthymiasterisk OR dysphoriasterisk).

RESULTS

On September 27, 2023, the PubMed search produced 223 articles, CINAHL 62, and the combined PsycLIT/ PsycARTICLES/PsycINFO/Psychology and Behavioral Sciences Collection search 36. Search on ClinicalTrials.gov produced 14 studies, of which none had available data. Eligible were peer-reviewed articles reporting psychiatric/psychological symptoms in patients with IC/BPS, i.e. 63 articles spanning from 2000 to October 2023. These studies identified depression and anxiety problems in the IC/BPS population, along with sleep problems and the tendency to catastrophizing.

CONCLUSION

Psychotherapies targeting catastrophizing and life stress emotional awareness and expression reduced perceived pain in women with IC/BPS. Such concepts should be considered when implementing treatments aimed at reducing IC/BPS-related pain.

Changes in brain structure and function during early aging in patients with chronic low back pain

Objective

To explore the structural and functional changes in cognition-related brain regions in patients with chronic low back pain (CLBP) at earlier ages, and explore the impact of the interaction between CLBP and age on the brain.

Methods

Seventy-six patients with CLBP were recruited and divided into "younger" age group (20-29 years, YA), "middle" age group (30-39 years, MA), and "older" age group (40-49 years, OA). All patients underwent functional magnetic resonance imaging (fMRI) as well as clinical psychological and pain-related symptoms assessments.

Results

Structural analysis showed that patients in OA group had lower gray matter (GM) volumes in the orbitofrontal cortex (OFC) bilaterally and the right superior frontal gyrus (SFG) compared to YA group. The resting-state brain activity analysis showed that amplitude of low-frequency fluctuation (ALFF) values in the bilateral postcentral gyrus and left ventral medial prefrontal cortex (mPFC) were significantly different in the OA group. The functional connectivity (FC) in the right ventral dorsolateral prefrontal cortex (DLPFC) and the right insula was significantly decreased in the OA group compared to the YA and MA groups. Likewise, the FC in the left caudal parahippocampal gyrus (PHG) and left inferior parietal lobule (IPL) were significantly lower in the MA and OA groups compared to the YA group. In addition, both the structural properties and the FC values of these brain regions were significantly correlated with age.

Conclusion

This preliminary study concludes that CLBP affects the aging process. The synergistic effects of CLBP and aging accelerate the functional and structural decline of certain areas of the brain, which not only affects pain processing, but are also may be associated with cognitive declines.

Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research

OBJECTIVE

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management.

METHODS

A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used.

RESULTS

After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP.

CONCLUSION

The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures.

SIGNIFICANCE

This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Altered effective connectivity from cerebellum to motor cortex in chronic low back pain: A multivariate pattern analysis and spectral dynamic causal modeling study

To explore the central processing mechanism of pain perception in chronic low back pain (cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional amplitude of low-frequency fluctuations (fALFF) analysis, and spectral dynamic causal modeling (spDCM). Thirty-two patients with cLBP and 29 matched healthy controls (HCs) for the first cohort and 24 patients with cLBP and 22 HCs for the validation cohort underwent resting-state fMRI scan. The alterations in static and dynamic fALFF were as classification features to distinguish patients with cLBP from HCs. The brain regions gotten from the MVPA results were used for further spDCM analysis. We found that the most discriminative brain regions that contributed to the classification were the right supplementary motor area (SMA.R), left paracentral lobule (PCL.L), and bilateral cerebellar Crus II. The spDCM results displayed decreased excitatory influence from the bilateral cerebellar Crus II to PCL.L in patients with cLBP compared with HCs. Moreover, the conversion of effective connectivity from the bilateral cerebellar Crus II to SMA.R from excitatory influence to inhibitive influence, and the effective connectivity strength exhibited partially mediated effects on Chinese Short Form Oswestry Disability Index Questionnaire (C-SFODI) scores. Our findings suggest that the cerebellum and its weakened or inhibited connections to the motor cortex may be one of the underlying feedback pathways for pain perception in cLBP, and partially mediate the degree of dysfunction.

Diagnosis of Chronic Musculoskeletal Pain by Using Functional Near-Infrared Spectroscopy and Machine Learning

Chronic pain (CP) has been found to cause significant alternations of the brain's structure and function due to changes in pain processing and disrupted cognitive functions, including with respect to the prefrontal cortex (PFC). However, until now, no studies have used a wearable, low-cost neuroimaging tool capable of performing functional near-infrared spectroscopy (fNIRS) to explore the functional alternations of the PFC and thus automatically achieve a clinical diagnosis of CP. In this case-control study, the pain characteristics of 19 chronic pain patients and 32 healthy controls were measured using fNIRS. Functional connectivity (FC), FC in the PFC, and spontaneous brain activity of the PFC were examined in the CP patients and compared to those of healthy controls (HCs). Then, leave-one-out cross-validation and machine learning algorithms were used to automatically achieve a diagnosis corresponding to a CP patient or an HC. The current study found significantly weaker FC, notably higher small-worldness properties of FC, and increased spontaneous brain activity during resting state within the PFC. Additionally, the resting-state fNIRS measurements exhibited excellent performance in identifying the chronic pain patients via supervised machine learning, achieving F1 score of 0.8229 using only seven features. It is expected that potential FC features can be identified, which can thus serve as a neural marker for the detection of CP using machine learning algorithms. Therefore, the present study will open a new avenue for the diagnosis of chronic musculoskeletal pain by using fNIRS and machine learning techniques.

Reduced Spinal Cord Gray Matter in Patients with Fibromyalgia Using Opioids Long-term

Objective

Chronic pain involves alterations in brain gray matter volume (GMV). Moreover, opioid medications are known to reduce GMV in numerous brain regions involved in pain processing. However, no research has evaluated (1) chronic pain-related GMV alterations in the spinal cord or (2) the effect of opioids on spinal cord GMV. Accordingly, this study evaluated spinal cord GMV in health controls and patients with fibromyalgia who were using and not using opioids long-term.

Methods

We analyzed average C5 - C7 GMV of the spinal cord dorsal and ventral horns in separate female cohorts of healthy controls (HC, n = 30), fibromyalgia patients not using opioids (FMN, n = 31), and fibromyalgia patients using opioids long-term (FMO, n = 27). To assess the effect of group on average dorsal and ventral horn GMV, we conducted a one-way multivariate analysis of covariance.

Results

After controlling for age, we observed a significant effect of group on ventral horn GMV (p = 0.03, η2 = 0.09), and on dorsal horn GMV (p = 0.05, η2 = 0.08). Tukey's posthoc comparisons showed that, compared to HC participants, FMOs had significantly lower ventral (p = 0.01) and dorsal (p = 0.02) GMVs. Among FMOs only, ventral horn GMV was significantly positively associated with pain severity and interference, and both dorsal and ventral GMVs were significantly positively associated with cold pain tolerance.

Conclusion

Long-term opioid use may impact sensory processing in fibromyalgia via gray matter changes within the cervical spinal cord.

Reproducible Microstructural Changes in the Brain Associated With the Presence and Severity of Urologic Chronic Pelvic Pain Syndrome (UCPPS): A 3-Year Longitudinal Diffusion Tensor Imaging Study From the MAPP Network

Microstructural alterations have been reported in patients with urologic chronic pelvic pain syndrome (UCPPS). However, it isn't clear whether these alterations are reproducible within 6 months or whether long-term symptom improvement is associated with specific microstructural changes. Using data from the MAPP-II Research Network, the current study performed population-based voxel-wise DTI and probabilistic tractography in a large sample of participants from the multicenter cohort with UCPPS (N = 364) and healthy controls (HCs, N = 61) over 36 months. While fractional anisotropy (FA) differences between UCPPS patients and HCs were observed to be unique at baseline and 6-month follow-up visits, consistent aberrations in mean diffusivity (MD) were observed between UCPPS and HCs at baseline and repeated at 6 months. Additionally, compared to HCs, UCPPS patients showed stronger structural connectivity (SC) between the left postcentral gyrus and the left precuneus, and weaker SC from the left cuneus to the left lateral occipital cortex and the isthmus of the left cingulate cortex at baseline and 6-month. By 36 months, reduced FA and MD aberrations in these same regions were associated with symptom improvement in UCPPS. Together, results suggest changes in white matter microstructure may play a role in the persistent pain symptoms in UCPPS. PERSPECTIVE: This longitudinal study identified reproducible, "disease-associated" patterns in altered mean diffusivity and abnormal microstructural connectivity in UCPPS comparing to HCs over 6 months. These differences were found in regions involved in sensory processing and integration and pain modulation, making it potentially amenable for clinical interventions that target synaptic and/or neuronal reorganization.

A Parabrachial-to-Amygdala Circuit That Determines Hemispheric Lateralization of Somatosensory Processing

BACKGROUND

The central amygdala (CeA) is a bilateral hub of pain and emotional processing with well-established functional lateralization. We reported that optogenetic manipulation of neural activity in the left and right CeA has opposing effects on bladder pain.

METHODS

To determine the influence of calcitonin gene-related peptide (CGRP) signaling from the parabrachial nucleus on this diametrically opposed lateralization, we administered CGRP and evaluated the activity of CeA neurons in acute brain slices as well as the behavioral signs of bladder pain in the mouse.

RESULTS

We found that CGRP increased firing in both the right and left CeA neurons. Furthermore, we found that CGRP administration in the right CeA increased behavioral signs of bladder pain and decreased bladder pain-like behavior when administered in the left CeA.

CONCLUSIONS

These studies reveal a parabrachial-to-amygdala circuit driven by opposing actions of CGRP that determines hemispheric lateralization of visceral pain.

Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analyses

INTRODUCTION

Sciatica is a pain disorder often caused by the herniated disk compressing the lumbosacral nerve roots. Neuroimaging studies have identified functional abnormalities in patients with chronic sciatica (CS). However, few studies have investigated the neural marker of CS using brain structure and the classification value of multidimensional neuroimaging features in CS patients is unclear.

METHODS

Here, structural and resting-state functional magnetic resonance imaging (fMRI) was acquired for 34 CS patients and 36 matched healthy controls (HCs). We analyzed cortical surface area, cortical thickness, amplitude of low-frequency fluctuation (ALFF), regional homogeneity (REHO), between-regions functional connectivity (FC), and assessed the correlation between neuroimaging measures and clinical scores. Finally, the multimodal neuroimaging features were used to differentiate the CS patients and HC individuals by support vector machine (SVM) algorithm.

RESULTS

Compared to HC, CS patients had a larger cortical surface area in the right banks of the superior temporal sulcus and rostral anterior cingulate; higher ALFF value in the left inferior frontal gyrus; enhanced FCs between somatomotor and ventral attention network. Three FCs values were associated with clinical pain scores. Furthermore, the three multimodal neuroimaging features with significant differences between groups and the SVM algorithm could classify CS patients and HC with an accuracy of 90.00%.

DISCUSSION

Together, our findings revealed extensive reorganization of local functional properties, surface area, and network metrics in CS patients. The success of patient identification highlights the potential of using artificial intelligence and multimodal neuroimaging markers in chronic pain research.

Pelvic Venous Disorders (PeVD)

Pelvic venous disorders (PeVDs) have replaced the concept of pelvic congestion syndrome encompassing venous origin chronic pelvic pain (VO-CPP) in women. The evaluation of women with VO-CPP includes the assessment for other causes of pelvic pain as well as imaging evaluation for pelvic varicosities measuring greater than 5 mm diameter, ovarian vein diameter, and flow direction, as well as iliac vein diameter and signs of compression. Proper identification of these patients can lead to high degrees of success eliminating chronic pelvic pain following ovarian vein embolization and/or iliac vein stenting. Strong encouragement is provided to use the symptoms, varices, pathophysiology classification for these patients and upcoming research studies on the specific symptoms of patients with VO-CPP will help elucidate patient selection for intervention. Additional future randomized controlled trials are also upcoming to evaluate for outcomes of ovarian vein embolization and iliac vein.

The functional connectivity of the basal ganglia subregions changed in mid-aged and young males with chronic prostatitis/chronic pelvic pain syndrome

Background

The Basal ganglia (BG) played a crucial role in the brain-level mechanisms of chronic pain disorders. However, the functional changes of BG in chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) are still poorly understood. This study investigated the BG subregions’ resting-state functional connectivity (rs-FC) in CP/CPPS patients compared with healthy controls.

Methods

Twenty eight patients with CP/CPPS and 28 age- and education-matched healthy males underwent clinical measurements and 3T brain MR imaging, including T1-weighted structural images and resting-state functional imaging. The data were analyzed by the seeded-based rs-FC analysis. Then, a machine learning method was applied to assess the feasibility of detecting CP/CPPS patients through the changed rs-FC.

Results

Compared with healthy males, patients presented decreased rs-FC between the BG subregions and right middle cingulate cortex, and correlated with pain (r = 0.51, p-uncorrected = 0.005) and urinary symptoms (r = –0.4, p-uncorrected = 0.034). The left superior temporal gyrus and right supramarginal gyrus showed decreased rs-FC with the BG subregions as well. The area under the receiver operating characteristic curve of 0.943 (accuracy = 80%, F1-score = 80.6%) was achieved for the classification of CP/CPPS patients and healthy males with support vector machine (SVM) based on the changed rs-FC.

Conclusion

These findings provide evidence of altered BG subregions’ rs-FC in CP/CPPS, which may contribute to our understanding of the BG’s role in CP/CPPS.

Neuroimaging Assessment of Pain

Pain is an unpleasant sensory and emotional experience. Understanding the neural mechanisms of acute and chronic pain and the brain changes affecting pain factors is important for finding pain treatment methods. The emergence and progress of non-invasive neuroimaging technology can help us better understand pain at the neural level. Recent developments in identifying brain-based biomarkers of pain through advances in advanced imaging can provide some foundations for predicting and detecting pain. For example, a neurologic pain signature (involving brain regions that receive nociceptive afferents) and a stimulus intensity-independent pain signature (involving brain regions that do not show increased activity in proportion to noxious stimulus intensity) were developed based on multivariate modeling to identify processes related to the pain experience. However, an accurate and comprehensive review of common neuroimaging techniques for evaluating pain is lacking. This paper reviews the mechanism, clinical application, reliability, strengths, and limitations of common neuroimaging techniques for assessing pain to promote our further understanding of pain.

The neurologic pain signature responds to nonsteroidal anti-inflammatory treatment vs placebo in knee osteoarthritis

Supplemental Digital Content is Available in the Text.

fMRI-based measures, validated for nociceptive pain, respond to acute osteoarthritis pain, are not sensitive to placebo, and are mild-to-moderately sensitive to naproxen.

Introduction:

Many drug trials for chronic pain fail because of high placebo response rates in primary endpoints. Neurophysiological measures can help identify pain-linked pathophysiology and treatment mechanisms. They can also help guide early stop/go decisions, particularly if they respond to verum treatment but not placebo. The neurologic pain signature (NPS), an fMRI-based measure that tracks evoked pain in 40 published samples and is insensitive to placebo in healthy adults, provides a potentially useful neurophysiological measure linked to nociceptive pain.

Objectives:

This study aims to validate the NPS in knee osteoarthritis (OA) patients and test the effects of naproxen on this signature.

Methods:

In 2 studies (50 patients, 64.6 years, 75% females), we (1) test the NPS and other control signatures related to negative emotion in knee OA pain patients; (2) test the effect of placebo treatments; and (3) test the effect of naproxen, a routinely prescribed nonsteroidal anti-inflammatory drug in OA.

Results:

The NPS was activated during knee pain in OA (d = 1.51, P < 0.001) and did not respond to placebo (d = 0.12, P = 0.23). A single dose of naproxen reduced NPS responses (vs placebo, NPS d = 0.34, P = 0.03 and pronociceptive NPS component d = 0.38, P = 0.02). Naproxen effects were specific for the NPS and did not appear in other control signatures.

Conclusion:

This study provides preliminary evidence that fMRI-based measures, validated for nociceptive pain, respond to acute OA pain, do not appear sensitive to placebo, and are mild-to-moderately sensitive to naproxen.

Decoding pain from brain activity

Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.

Clinical Presentation and Evaluation of Pelvic Venous Disorders in Women

The term pelvic venous disorders (PeVD) describes a group of related clinical entities with overlapping clinical presentations that were previously characterized by separate imprecise syndromic terminology. The clinical manifestations of PeVD may variously include chronic pelvic pain; pelvic origin extrapelvic lower extremity and genital varices; lower extremity pain and swelling; and left flank pain and hematuria. This manuscript focuses on the primary manifestations of PeVD in women - chronic pelvic pain and pelvic origin lower extremity and vulvar varices - and will review the underlying pathophysiology and related complicating factors (such as maladaptive pain responses) to explain the variety of clinical presentations.

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.

Alexithymia Is Associated with Altered Cortical Thickness Networks in the General Population

BACKGROUND

Alexithymia is a personality trait characterized by difficulties in identifying and describing emotions and associated with various psychiatric disorders. Neuroimaging studies found evidence for morphological and functional brain alterations in alexithymic subjects. However, the neurobiological mechanisms underlying alexithymia remain incompletely understood.

METHODS

We study the association of alexithymia with cortical correlation networks in a large community-dwelling sample of the Study of Health in Pomerania. Our analysis includes data of n = 2,199 individuals (49.4% females, age = 52.1 ± 13.6 years) which were divided into a low and high alexithymic group by a median split of the Toronto Alexithymia Scale. Cortical correlation networks were constructed based on the mean thicknesses of 68 regions, and differences in centralities were investigated.

RESULTS

We found a significantly increased centrality of the right paracentral lobule in the high alexithymia network after correction for multiple testing. Several other regions with motoric and sensory functions showed altered centrality on a nominally significant level.

CONCLUSIONS

Finding increased centrality of the paracentral lobule, a brain area with sensory as well as motoric features and involvement in bowel and bladder voiding, may contribute to explain the association of alexithymia with functional somatic disorders and chronic pain syndromes.

Multi-modal biomarkers of low back pain: A machine learning approach

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Widespread differences in cortical thickness (CT) were observed in patients with low back pain.

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Changes in CT correlated with self-reported clinical scores of pain and emotion.

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Changes in resting state fMRI metrics of functional networks.

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Support vector machines separated low back pain patients from controls with a high performance.

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Multi-modal biomarkers can be useful when identifying personalized treatments for low back pain.

Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66–0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy.

Magnetic resonance imaging for chronic pain: diagnosis, manipulation, and biomarkers

Pain is a multidimensional subjective experience with biological, psychological, and social factors. Whereas acute pain can be a warning signal for the body to avoid excessive injury, long-term and ongoing pain may be developed as chronic pain. There are more than 100 million people in China living with chronic pain, which has raised a huge socioeconomic burden. Studying the mechanisms of pain and developing effective analgesia approaches are important for basic and clinical research. Recently, with the development of brain imaging and data analytical approaches, the neural mechanisms of chronic pain have been widely studied. In the first part of this review, we briefly introduced the magnetic resonance imaging and conventional analytical approaches for brain imaging data. Then, we reviewed brain alterations caused by several chronic pain disorders, including localized and widespread primary pain, primary headaches and orofacial pain, musculoskeletal pain, and neuropathic pain, and present meta-analytical results to show brain regions associated with the pathophysiology of chronic pain. Next, we reviewed brain changes induced by pain interventions, such as pharmacotherapy, neuromodulation, and acupuncture. Lastly, we reviewed emerging studies that combined advanced machine learning and neuroimaging techniques to identify diagnostic, prognostic, and predictive biomarkers in chronic pain patients.

Sensitivity of functional connectivity to periaqueductal gray localization, with implications for identifying disease-related changes in chronic visceral pain: A MAPP Research Network neuroimaging study

Previous studies examining the resting-state functional connectivity of the periaqueductal gray (PAG) in chronic visceral pain have localized PAG coordinates derived from BOLD responses to provoked acute pain. These coordinates appear to be several millimeters anterior of the anatomical location of the PAG. Therefore, we aimed to determine whether measures of PAG functional connectivity are sensitive to the localization technique, and if the localization approach has an impact on detecting disease-related differences in chronic visceral pain patients. We examined structural and resting-state functional MRI (rs-fMRI) images from 209 participants in the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. We applied three different localization techniques to define a region-of-interest (ROI) for the PAG: 1) a ROI previously-published as a Montreal Neurological Institute (MNI) coordinate surrounded by a 3 mm radius sphere (MNI-sphere), 2) a ROI that was hand-traced over the PAG in a MNI template brain (MNI-trace), and 3) a ROI that was hand-drawn over the PAG in structural images from 30 individual participants (participant-trace). We compared the correlation among the rs-fMRI signals from these PAG ROIs, as well as the functional connectivity of these ROIs with the whole brain. First, we found important non-uniformities in brainstem rs-fMRI signals, as rs-fMRI signals from the MNI-trace ROI were significantly more similar to the participant-trace ROI than to the MNI-sphere ROI. We then found that choice of ROI also impacts whole-brain functional connectivity, as measures of PAG functional connectivity throughout the brain were more similar between MNI-trace and participant-trace compared to MNI-sphere and participant-trace. Finally, we found that ROI choice impacts detection of disease-related differences, as functional connectivity differences between pelvic pain patients and healthy controls were much more apparent using the MNI-trace ROI compared to the MNI-sphere ROI. These results indicate that the ROI used to localize the PAG is critical, especially when examining brain functional connectivity changes in chronic visceral pain patients.

Left and right hemispheric lateralization of the amygdala in pain

Hemispheric asymmetries within the brain have been identified across taxa and have been extensively studied since the early 19th century. Here, we discuss lateralization of a brain structure, the amygdala, and how this lateralization is reshaping how we understand the role of the amygdala in pain processing. The amygdala is an almond-shaped, bilateral brain structure located within the limbic system. Historically, the amygdala was known to have a role in the processing of emotions and attaching emotional valence to memories and other experiences. The amygdala has been extensively studied in fear conditioning and affect but recently has been shown to have an important role in processing noxious information and impacting pain. The amygdala is composed of multiple nuclei; of special interest is the central nucleus of the amygdala (CeA). The CeA receives direct nociceptive inputs from the parabrachial nucleus (PBN) through the spino-parabrachio-amygdaloid pathway as well as more highly processed cortical and thalamic input via the lateral and basolateral amygdala. Although the amygdala is a bilateral brain region, most data investigating the amygdala's role in pain have been generated from the right CeA, which has an overwhelmingly pro-nociceptive function across pain models. The left CeA has often been characterized to have no effect on pain modulation, a dampened pro-nociceptive function, or most recently an anti-nociceptive function. This review explores the current literature on CeA lateralization and the hemispheres' respective roles in the processing and modulation of different forms of pain.

Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Ceftriaxone inhibits stress-induced bladder hyperalgesia and alters cerebral micturition and nociceptive circuits in the rat: A multidisciplinary approach to the study of urologic chronic pelvic pain syndrome research network study

AIMS

Emotional stress plays a role in the exacerbation and development of interstitial cystitis/bladder pain syndrome (IC/BPS). Given the significant overlap of brain circuits involved in stress, anxiety, and micturition, and the documented role of glutamate in their regulation, we examined the effects of an increase in glutamate transport on central amplification of stress-induced bladder hyperalgesia, a core feature of IC/BPS.

METHODS

Wistar-Kyoto rats were exposed to water avoidance stress (WAS, 1 hour/day x 10 days) or sham stress, with subgroups receiving daily administration of ceftriaxone (CTX), an activator of glutamate transport. Thereafter, cystometrograms were obtained during bladder infusion with visceromotor responses (VMR) recorded simultaneously. Cerebral blood flow (CBF) mapping was performed by intravenous injection of [¹⁴ C]-iodoantipyrine during passive bladder distension. Regional CBF was quantified in autoradiographs of brain slices and analyzed in three dimensional reconstructed brains with statistical parametric mapping.

RESULTS

WAS elicited visceral hypersensitivity during bladder filling as demonstrated by a decreased pressure threshold and VMR threshold triggering the voiding phase. Brain maps revealed stress effects in regions noted to be responsive to bladder filling. CTX diminished visceral hypersensitivity and attenuated many stress-related cerebral activations within the supraspinal micturition circuit and in overlapping limbic and nociceptive regions, including the posterior midline cortex (posterior cingulate/anterior retrosplenium), somatosensory cortex, and anterior thalamus.

CONCLUSIONS

CTX diminished bladder hyspersensitivity and attenuated regions of the brain that contribute to nociceptive and micturition circuits, show stress effects, and have been reported to demonstrated altered functionality in patients with IC/BPS. Glutamatergic pharmacologic strategies modulating stress-related bladder dysfunction may be a novel approach to the treatment of IC/BPS.

Urologic chronic pelvic pain syndrome: insights from the MAPP Research Network

Urologic chronic pelvic pain syndrome (UCPPS), which encompasses interstitial cystitis/bladder pain syndrome and chronic prostatitis/chronic pelvic pain syndrome, is characterized by chronic pain in the pelvic region or genitalia that is often accompanied by urinary frequency and urgency. Despite considerable research, no definite aetiological risk factors or effective treatments have been identified. The Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network uses a novel integrated strategy to characterize UCPPS as a systemic disorder that potentially involves multiple aetiologies. The first phase, MAPP I, included >1,000 participants who completed an intensive baseline assessment followed by a 12-month observational follow-up period. MAPP I studies showed that UCPPS pain and urinary symptoms co-vary, with only moderate correlation, and should be evaluated separately and that symptom flares are common and can differ considerably in intensity, duration and influence on quality of life. Longitudinal clinical changes in UCPPS correlated with structural and functional brain changes, and many patients experienced global multisensory hypersensitivity. Additionally, UCPPS symptom profiles were distinguishable by biological correlates, such as immune factors. These findings indicate that patients with UCPPS have objective phenotypic abnormalities and distinct biological characteristics, providing a new foundation for the study and clinical management of UCPPS.

Central Nervous System Changes in Pelvic Inflammation/Pain Patients

Purpose of Review

Centralized pain syndromes (CPS), including chronic pelvic pain (CPP) syndrome, are significant public health problems with prevalence more than diabetes, cancer, or cardiovascular disease. A variety of pathologies are linked with CPP syndrome; however, pain often continues without the presence of pathology, or when an underlying pelvic disease is found, the extent and severity of pain are disproportionate. Although this is not a systematic review, we performed a detailed literature search to identify relevant papers and to provide the available evidence for central changes in association with CPP syndrome.

Recent Findings

Recent advances in brain imaging techniques have provided more accurate data on gray matter volume, functional connectivity, and metabolite levels in the pain-relevant areas of the brain. The present evidence shows that like other chronic pain conditions, the CPP syndrome is associated with central nervous system (CNS) alterations. In particular, these include changes in brain structure, in the activity of both the hypothalamic–pituitary–adrenal (HPA) axis and the autonomic nervous system, and in the behavioral and central response to noxious stimulation.

Summary

A growing body of evidence, mostly from neuroimaging, suggests that for many patients with CPP, the pain may be associated to changes in both structure and function of the CNS. The treatment of pain symptoms, even without the presence of identifiable pathology, may prevent the development or at least minimize the progression of long-term central changes. These findings support the use of new therapeutic strategies targeting the CNS for controlling of pain in CPP conditions.

Evidence for decreased Neurologic Pain Signature activation following thoracic spinal manipulation in healthy volunteers and participants with neck pain

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The use of brain-based models of pain were explored in two clinical studies.

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Neurologic pain signature activation decreased following spinal manipulation.

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Spinal manipulation altered the processing of pain-related brain activity.

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We provide evidence for a centrally mediated therapeutic action of spinal manipulation.

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Brain-based models have potential as objective clinical biomarkers of pain.

Background Context

Spinal manipulation (SM) is a common treatment for neck and back pain, theorized to mechanically affect the spine leading to therapeutic mechanical changes. The link between specific mechanical effects and clinical improvement is not well supported. SM's therapeutic action may instead be partially mediated within the central nervous system.

Purpose

To introduce brain-based models of pain for spinal pain and manual therapy research, characterize the distributed central mechanisms of SM, and advance the preliminary validation of brain-based models as potential clinical biomarkers of pain.

Study Design

Secondary analysis of two functional magnetic resonance imaging studies investigating the effect of thoracic SM on pain-related brain activity: A non-controlled, non-blinded study in healthy volunteers (Study 1, n = 10, 5 females, and mean age = 31.2 ± 10.0 years) and a randomized controlled study in participants with acute to subacute neck pain (Study 2, n = 24, 16 females, mean age = 38.0 ± 15.1 years).

Methods

Functional magnetic resonance imaging was performed during noxious mechanical stimulation of the right index finger cuticle pre- and post-intervention. The effect of SM on pain-related activity was studied within brain regions defined by the Neurologic Pain Signature (NPS) that are predictive of physical pain.

Results

In Study 1, evoked mechanical pain (p < 0.001) and NPS activation (p = 0.010) decreased following SM, and the changes in evoked pain and NPS activation were correlated (r_RM² = 0.418, p = 0.016). Activation within the NPS subregions of the dorsal anterior cingulate cortex (dACC, p = 0.012) and right secondary somatosensory cortex/operculum (rS2_Op, p = 0.045) also decreased following SM, and evoked pain was correlated with dACC activity (r_RM² = 0.477, p = 0.019). In Study 2, neck pain (p = 0.046) and NPS (p = 0.033) activation decreased following verum but not sham SM. Associations between evoked pain, neck pain, and NPS activation, were not significant and less clear, possibly due to inadequate power, methodological limitations, or other confounding factors.

Conclusions

The findings provide preliminary evidence that SM may alter the processing of pain-related brain activity within specific pain-related brain regions and support the use of brain-based models as clinical biomarkers of pain.

Neuroimaging of Pain: Human Evidence and Clinical Relevance of Central Nervous System Processes and Modulation

Neuroimaging research has demonstrated definitive involvement of the central nervous system in the development, maintenance, and experience of chronic pain. Structural and functional neuroimaging has helped elucidate central nervous system contributors to chronic pain in humans. Neuroimaging of pain has provided a tool for increasing our understanding of how pharmacologic and psychologic therapies improve chronic pain. To date, findings from neuroimaging pain research have benefitted clinical practice by providing clinicians with an educational framework to discuss the biopsychosocial nature of pain with patients. Future advances in neuroimaging-based therapeutics (e.g., transcranial magnetic stimulation, real-time functional magnetic resonance imaging neurofeedback) may provide additional benefits for clinical practice. In the future, with standardization and validation, brain imaging could provide objective biomarkers of chronic pain, and guide treatment for personalized pain management. Similarly, brain-based biomarkers may provide an additional predictor of perioperative prognoses.

Neuroimaging-based pain biomarkers: definitions, clinical and research applications, and evaluation frameworks to achieve personalized pain medicine

One of the key ambitions of neuroimaging-based pain biomarker research is to augment patient and clinician reporting of clinically relevant phenomena with neural measures for prediction, prognosis, and detection of pain. Despite years of productive research on the neuroimaging of pain, such applications have seen little advancement. However, recent developments in identifying brain-based biomarkers of pain through advances in technology and multivariate pattern analysis provide some optimism. Here, we (1) define and review the different types of potential neuroimaging-based biomarkers, their clinical and research applications, and their limitations and (2) describe frameworks for evaluation of pain biomarkers used in other fields (eg, genetics, cancer, cardiovascular disease, immune system disorders, and rare diseases) to achieve broad clinical and research utility and minimize the risks of misapplication of this emerging technology. To conclude, we discuss future directions for neuroimaging-based biomarker research to achieve the goal of personalized pain medicine.

Neuroimaging-based biomarkers for pain: state of the field and current directions

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.

Identifying brain regions associated with the neuropathology of chronic low back pain: a resting-state amplitude of low-frequency fluctuation study

BACKGROUND

Previous studies have found widespread pain processing alterations in the brain in chronic low back pain (cLBP) patients. We aimed to (1) identify brain regions showing altered amplitude of low-frequency fluctuations (ALFF) using MRI and use these regions to discriminate cLBP patients from healthy controls (HCs) and (2) identify brain regions that are sensitive to cLBP pain intensity changes.

METHODS

We compared ALFF differences by MRI between cLBP subjects (90) and HCs (74), conducted a discriminative analysis to validate the results, and explored structural changes in key brain regions of cLBP. We also compared ALFF changes in cLBP patients after pain-exacerbating manoeuvres.

RESULTS

ALFF was increased in the post-/precentral gyrus (PoG/PrG), paracentral lobule (PCL)/supplementary motor area (SMA), and anterior cingulate cortex (ACC), and grey matter volume was increased in the left ACC in cLBP patients. PCL/SMA ALFF reliably discriminated cLBP patients from HCs in an independent cohort. cLBP patients showed increased ALFF in the insula, amygdala, hippocampal/parahippocampal gyrus, and thalamus and decreased ALFF in the default mode network (DMN) when their spontaneous low back pain intensity increased after the pain-exacerbating manoeuvre.

CONCLUSIONS

Brain low-frequency oscillations in the PCL, SMA, PoG, PrG, and ACC may be associated with the neuropathology of cLBP. Low-frequency oscillations in the insula, amygdala, hippocampal/parahippocampal gyrus, thalamus, and DMN are sensitive to manoeuvre-induced spontaneous back pain intensity changes.

Altered gray matter volume in sensorimotor and thalamic regions associated with pain in localized provoked vulvodynia: a voxel-based morphometry study

Multimodal neuroimaging studies provide support for a role of alterations in sensory processing circuits and endogenous pain modulatory systems in provoked vestibulodynia (PVD). In this study, we tested the hypotheses that PVD compared with healthy controls (HCs) would demonstrate gray matter volume (GMV) alterations in regions associated with sensorimotor, corticothalamic, and basal ganglia circuits. We also tested the replicability of previously reported gray matter increases in basal ganglia and hippocampal volumes in PVD vs HCs. In addition, disease specificity of GMV alterations were examined by comparing PVD with another chronic pain disorder. Finally, we examine whether GMV alterations are correlated with symptom measures. Structural magnetic resonance imaging was obtained in 119 premenopausal women (45 PVD, 45 HCs, and 29 irritable bowel syndrome [IBS]). A voxel-based morphometry analysis was applied to determine group differences in the hypothesized regions of interest. Compared with HCs, PVD women exhibited greater GMV in the basal ganglia, hippocampus, and sensorimotor cortices. Compared to patients with IBS, women with PVD had greater GMV in the hippocampus, and sensorimotor network, but lower GMV in the thalamus and precentral gyrus. Regional GMV alterations were associated with patient reports of pain during intercourse and muscle tenderness. The current findings provide further evidence that GMV is increased in PVD compared with HCs in several regions of the sensorimotor network and the hippocampus in patients with PVD. In addition, GMV distinct alterations in the sensorimotor network were identified between 2 pelvic pain disorders, PVD compared with IBS.

Whole-brain structural magnetic resonance imaging-based classification of primary dysmenorrhea in pain-free phase: a machine learning study

To develop a machine learning model to investigate the discriminative power of whole-brain gray-matter (GM) images derived from primary dysmenorrhea (PDM) women and healthy controls (HCs) during the pain-free phase and further evaluate the predictive ability of contributing features in predicting the variance in menstrual pain intensity. Sixty patients with PDM and 54 matched female HCs were recruited from the local university. All participants underwent the head and pelvic magnetic resonance imaging scans to calculate GM volume and myometrium-apparent diffusion coefficient (ADC) during their periovulatory phase. Questionnaire assessment was also conducted. A support vector machine algorithm was used to develop the classification model. The significance of model performance was determined by the permutation test. Multiple regression analysis was implemented to explore the relationship between discriminative features and intensity of menstrual pain. Demographics and myometrium ADC-based classifications failed to pass the permutation tests. Brain-based classification results demonstrated that 75.44% of subjects were correctly classified, with 83.33% identification of the patients with PDM (P < 0.001). In the regression analysis, demographical indicators and myometrium ADC accounted for a total of 29.37% of the variance in pain intensity. After regressing out these factors, GM features explained 60.33% of the remaining variance. Our results suggested that GM volume can be used to discriminate patients with PDM and HCs during the pain-free phase, and neuroimaging features can further predict the variance in the intensity of menstrual pain, which may provide a potential imaging marker for the assessment of menstrual pain intervention.

Impact of early adverse life events and sex on functional brain networks in patients with urological chronic pelvic pain syndrome (UCPPS): A MAPP Research Network study

Pain is a highly complex and individualized experience with biopsychosocial components. Neuroimaging research has shown evidence of the involvement of the central nervous system in the development and maintenance of chronic pain conditions, including urological chronic pelvic pain syndrome (UCPPS). Furthermore, a history of early adverse life events (EALs) has been shown to adversely impact symptoms throughout childhood and into adulthood. However, to date, the role of EAL's in the central processes of chronic pain have not been adequately investigated. We studied 85 patients (56 females) with UCPPS along with 86 healthy controls (HCs) who had resting-state magnetic resonance imaging scans (59 females), and data on EALs as a part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network Study. We used graph theory methods in order to investigate the impact of EALs on measures of centrality, which characterize information flow, communication, influence, and integration in a priori selected regions of interest. Patients with UCPPS exhibited lower centrality in the right anterior insula compared to HCs, a key node in the salience network. Males with UCPPS exhibited lower centrality in the right anterior insula compared the HC males. Females with UCPPS exhibited greater centrality in the right caudate nucleus and left angular gyrus compared to HC females. Males with UCPPS exhibited lower centrality in the left posterior cingulate, angular gyrus, middle temporal gyrus, and superior temporal sulcus, but greater centrality in the precuneus and anterior mid-cingulate cortex (aMCC) compared to females with UCPPS. Higher reports of EALs was associated with greater centrality in the left precuneus and left aMCC in females with UCPPS. This study provides evidence for disease and sex-related alterations in the default mode, salience, and basal ganglia networks in patients with UCPPS, which are moderated by EALs, and associated with clinical symptoms and quality of life (QoL).

Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia

Trigeminal neuralgia (TN) is a severe form of chronic facial neuropathic pain. Increasing interest in the neuroimaging of pain has highlighted changes in the root entry zone in TN, but also group-level central nervous system gray and white matter (WM) abnormalities. Group differences in neuroimaging data are frequently evaluated with univariate statistics; however, this approach is limited because it is based on single, or clusters of, voxels. By contrast, multivariate pattern analyses consider all the model's neuroanatomical features to capture a specific distributed spatial pattern. This approach has potential use as a prediction tool at the individual level. We hypothesized that a multivariate pattern classification method can distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs). Diffusion-weighted scans in 23 right-sided TN and matched controls were processed to extract whole-brain interregional streamlines. We used a linear support vector machine algorithm to differentiate interregional normalized streamline count between TN and HC. This algorithm successfully differentiated between TN and HC with an accuracy of 88%. The structural pattern emphasized WM connectivity of regions that subserve sensory, affective, and cognitive dimensions of pain, including the insula, precuneus, inferior and superior parietal lobules, and inferior and medial orbital frontal gyri. Normalized streamline counts were associated with longer pain duration and WM metric abnormality between the connections. This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN and highlights the role of structural brain imaging for identification of neuroanatomical features associated with neuropathic pain disorders.

Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy

INTRODUCTION

Artificial intelligence (AI) is a field of mathematical engineering which has potential to enhance healthcare through new care delivery strategies, informed decision making and facilitation of patient engagement. Machine learning (ML) is a form of narrow artificial intelligence which can be used to automate decision making and make predictions based upon patient data.

PURPOSE

This review outlines key applications of supervised and unsupervised machine learning in musculoskeletal medicine; such as diagnostic imaging, patient measurement data, and clinical decision support. The current literature base is examined to identify areas where ML performs equal to or more accurately than human levels.

IMPLICATIONS

Potential is apparent for intelligent machines to enhance various areas of physiotherapy practice through automization of tasks which involve data analysis, classification and prediction. Changes to service provision through applications of ML, should encourage physiotherapists to increase their awareness of and experiences with emerging technologies. Data literacy should be a component of professional development plans to assist physiotherapists in the application of ML and the preparation of information technology systems to use these techniques.

Modeling Neural Behavior and Pain During Bladder Distention using an Agent-based Model of the Central Nucleus of the Amygdala

Chronic bladder pain evokes asymmetric behavior in neurons across the left and right hemispheres of the amygdala. An agent-based computational model was created to simulate the firing of neurons over time and in response to painful bladder stimulation. Each agent represents one neuron and is characterized by its location in the amygdala and response type (excited or inhibited). At each time step, the firing rates (Hz) of all neurons are stochastically updated from probability distributions estimated from data collected in laboratory experiments. A damage accumulation model tracks the damage accrued by neurons during long-term, painful bladder stimulation. Emergent model output uses neural activity to measure temporal changes in pain attributed to bladder stimulation. Simulations demonstrate the model's ability to capture acute and chronic pain and its potential to predict changes in pain similar to those observed in the lab. Asymmetric neural activity during the progression of chronic pain is examined using model output and a sensitivity analysis.

Changes in brain white matter structure are associated with urine proteins in urologic chronic pelvic pain syndrome (UCPPS): A MAPP Network study

The Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network has yielded neuroimaging and urinary biomarker findings that highlight unique alterations in brain structure and in urinary proteins related to tissue remodeling and vascular structure in patients with Urological Chronic Pelvic Pain Syndrome (UCPPS). We hypothesized that localized changes in diffusion tensor imaging (DTI) measurements might be associated with corresponding changes in urinary protein levels in UCPPS. To test this hypothesis, we created statistical parameter maps depicting the linear correlation between DTI measurements (fractional anisotropy (FA) and apparent diffusion coefficient (ADC)) and urinary protein quantification (MMP2, MMP9, NGAL, MMP9/NGAL complex, and VEGF) in 30 UCPPS patients from the MAPP Research Network, after accounting for clinical covariates. Results identified a brainstem region that showed a strong correlation between both ADC (R² = 0.49, P<0.0001) and FA (R² = 0.39, P = 0.0002) with urinary MMP9 levels as well as a correlation between both ADC (R² = 0.42, P = 0.0001) and FA (R² = 0.29, P = 0.0020) and urinary MMP9/NGAL complex. Results also identified significant correlations between FA and urinary MMP9 in white matter adjacent to sensorimotor regions (R² = 0.30, P = 0.002; R² = 0.36, P = 0.0005, respectively), as well as a correlation in similar sensorimotor regions when examining ADC and urinary MMP2 levels (R² = 0.42, P<0.0001) as well as FA and urinary MMP9/NGAL complex (R² = 0.33, P = 0.0008). A large, diffuse cluster of white matter was identified as having a strong correlation between both ADC (R² = 0.35, P = 0.0006) and FA (R² = 0.43, P<0.0001) with urinary NGAL levels. In contrast, no significant association between DTI measurements and VEGF was observed. Results suggest that elevated MMP9 or MMP9/NGAL in UCPPS may be related to degenerative neuronal changes in brainstem nuclei through excitotoxicity, while also facilitating synaptic plasticity in sensorimotor regions.

ACR Appropriateness Criteria® Postmenopausal Subacute or Chronic Pelvic Pain

Pelvic pain is common in both reproductive age and postmenopausal women, and the major etiologies change throughout the life cycle. Chronic pain is defined as lasting for at least 6 months. There are many gastrointestinal and urinary disorders associated with chronic pain in this age group, which are not discussed in this guideline. Pain may be localized to the deep pelvis, with potential causes including pelvic congestion syndrome, intraperitoneal adhesions, hydrosalpinx, chronic inflammatory disease, or cervical stenosis. Ultrasound is the initial imaging modality of choice, while CT and MRI may be appropriate for further characterization of sonographic findings. Alternatively, pain may be localized to the vagina, vulva, or perineum, with potential causes including vaginal atrophy, vaginismus, vaginal or vulvar cysts, vulvodynia, or pelvic myofascial pain. Imaging is primarily indicated in context of an abnormal physical exam and ultrasound is the initial modality of choice, while MRI may be appropriate for further characterization in select cases. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

Pain Neuroimaging in Humans: A Primer for Beginners and Non-Imagers

Human pain neuroimaging has exploded in the past 2 decades. During this time, the broader neuroimaging community has continued to investigate and refine methods. Another key to progress is exchange with clinicians and pain scientists working with other model systems and approaches. These collaborative efforts require that non-imagers be able to evaluate and assess the evidence provided in these reports. Likewise, new trainees must design rigorous and reliable pain imaging experiments. In this article we provide a guideline for designing, reading, evaluating, analyzing, and reporting results of a pain neuroimaging experiment, with a focus on functional and structural magnetic resonance imaging. We focus in particular on considerations that are unique to neuroimaging studies of pain in humans, including study design and analysis, inferences that can be drawn from these studies, and the strengths and limitations of the approach.

Relationships between brain metabolite levels, functional connectivity, and negative mood in urologic chronic pelvic pain syndrome patients compared to controls: A MAPP research network study

Until recently, the predominant pathology of chronic pelvic pain conditions was thought to reside in the peripheral tissues. However, mounting evidence from neuroimaging studies suggests an important role of the central nervous system in the pathogenesis of these conditions. In the present cross-sectional study, proton magnetic resonance spectroscopy (¹H-MRS) of the brain was conducted in female patients with urologic chronic pelvic pain syndrome (UCPPS) to determine if they exhibit abnormal concentrations of brain metabolites (e.g. those indicative of heightened excitatory tone) in regions involved in the processing and modulation of pain, including the anterior cingulate cortex (ACC) and the anterior and posterior insular cortices. Compared to a group of age-matched healthy subjects, there were significantly higher levels of choline (p = 0.006, uncorrected) in the ACC of UCPPS patients. ACC choline levels were therefore compared with the region's resting functional connectivity to the rest of the brain. Higher choline was associated with greater ACC-to-limbic system connectivity in UCPPS patients, contrasted with lower connectivity in controls (i.e. an interaction). In patients, ACC choline levels were also positively correlated with negative mood. ACC γ-aminobutyric acid (GABA) levels were lower in UCPPS patients compared with controls (p = 0.02, uncorrected), but this did not meet statistical correction for the 4 separate regional comparisons of metabolites. These results are the first to uncover abnormal GABA and choline levels in the brain of UCPPS patients compared to controls. Low GABA levels have been identified in other pain syndromes and might contribute to CNS hyper-excitability in these conditions. The relationships between increased ACC choline levels, ACC-to-limbic connectivity, and negative mood in UCPPS patients suggest that this metabolite could be related to the affective symptomatology of this syndrome.

Divergent functions of the left and right central amygdala in visceral nociception

The left and right central amygdalae (CeA) are limbic regions involved in somatic and visceral pain processing. These 2 nuclei are asymmetrically involved in somatic pain modulation; pain-like responses on both sides of the body are preferentially driven by the right CeA, and in a reciprocal fashion, nociceptive somatic stimuli on both sides of the body predominantly alter molecular and physiological activities in the right CeA. Unknown, however, is whether this lateralization also exists in visceral pain processing and furthermore what function the left CeA has in modulating nociceptive information. Using urinary bladder distension (UBD) and excitatory optogenetics, a pronociceptive function of the right CeA was demonstrated in mice. Channelrhodopsin-2-mediated activation of the right CeA increased visceromotor responses (VMRs), while activation of the left CeA had no effect. Similarly, UBD-evoked VMRs increased after unilateral infusion of pituitary adenylate cyclase-activating polypeptide in the right CeA. To determine intrinsic left CeA involvement in bladder pain modulation, this region was optogenetically silenced during noxious UBD. Halorhodopsin (NpHR)-mediated inhibition of the left CeA increased VMRs, suggesting an ongoing antinociceptive function for this region. Finally, divergent left and right CeA functions were evaluated during abdominal mechanosensory testing. In naive animals, channelrhodopsin-2-mediated activation of the right CeA induced mechanical allodynia, and after cyclophosphamide-induced bladder sensitization, activation of the left CeA reversed referred bladder pain-like behaviors. Overall, these data provide evidence for functional brain lateralization in the absence of peripheral anatomical asymmetries.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations

Chronic pain is the greatest source of disability globally and claims related to chronic pain feature in many insurance and medico-legal cases. Brain imaging (for example, functional MRI, PET, EEG and magnetoencephalography) is widely considered to have potential for diagnosis, prognostication, and prediction of treatment outcome in patients with chronic pain. In this Consensus Statement, a presidential task force of the International Association for the Study of Pain examines the capabilities of brain imaging in the diagnosis of chronic pain, and the ethical and legal implications of its use in this way. The task force emphasizes that the use of brain imaging in this context is in a discovery phase, but has the potential to increase our understanding of the neural underpinnings of chronic pain, inform the development of therapeutic agents, and predict treatment outcomes for use in personalized pain management. The task force proposes standards of evidence that must be satisfied before any brain imaging measure can be considered suitable for clinical or legal purposes. The admissibility of such evidence in legal cases also strongly depends on laws that vary between jurisdictions. For these reasons, the task force concludes that the use of brain imaging findings to support or dispute a claim of chronic pain - effectively as a pain lie detector - is not warranted, but that imaging should be used to further our understanding of the mechanisms underlying pain.

Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: a MAPP network study

Chronic pain symptoms often change over time, even in individuals who have had symptoms for years. Studying biological factors that predict trends in symptom change in chronic pain may uncover novel pathophysiological mechanisms and potential therapeutic targets. In this study, we investigated whether brain functional connectivity measures obtained from resting-state functional magnetic resonance imaging at baseline can predict longitudinal symptom change (3, 6, and 12 months after scan) in urologic chronic pelvic pain syndrome. We studied 52 individuals with urologic chronic pelvic pain syndrome (34 women, 18 men) who had baseline neuroimaging followed by symptom tracking every 2 weeks for 1 year as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. We found that brain functional connectivity can make a significant prediction of short-term (3 month) pain reduction with 73.1% accuracy (69.2% sensitivity and 75.0% precision). In addition, we found that the brain regions with greatest contribution to the classification were preferentially aligned with the left frontoparietal network. Resting-state functional magnetic resonance imaging measures seemed to be less informative about 6- or 12-month symptom change. Our study provides the first evidence that future trends in symptom change in patients in a state of chronic pain may be linked to functional connectivity within specific brain networks.

Characterization of Whole Body Pain in Urological Chronic Pelvic Pain Syndrome at Baseline: A MAPP Research Network Study

PURPOSE

We characterized the location and spatial distribution of whole body pain in patients with urological chronic pelvic pain syndrome using a body map. We also compared the severity of urinary symptoms, pelvic pain, nonpelvic pain and psychosocial health among patients with different pain patterns.

MATERIALS AND METHODS

A total of 233 women and 191 men with urological chronic pelvic pain syndrome enrolled in a multicenter, 1-year observational study completed a battery of baseline measures, including a body map describing the location of pain during the last week. Participants were categorized with pelvic pain if they reported pain in the abdomen and pelvis only. Participants who reported pain beyond the pelvis were further divided into 2 subgroups based on the number of broader body regions affected by pain, including an intermediate group with 1 or 2 additional regions outside the pelvis and a widespread pain group with 3 to 7 additional regions.

RESULTS

Of the 424 enrolled patients 25% reported pelvic pain only and 75% reported pain beyond the pelvis, of whom 38% reported widespread pain. Participants with a greater number of pain locations had greater nonpelvic pain severity (p <0.0001), sleep disturbance (p = 0.035), depression (p = 0.005), anxiety (p = 0.011), psychological stress (p = 0.005) and negative affect scores (p = 0.0004), and worse quality of life (p ≤0.021). No difference in pelvic pain and urinary symptom severity was observed according to increasing pain distribution.

CONCLUSIONS

Three-quarters of the men and women with urological chronic pelvic pain syndrome reported pain outside the pelvis. Widespread pain was associated with greater severity of nonpelvic pain symptoms, poorer psychosocial health and worse quality of life but not with worse pelvic pain or urinary symptoms.