Pain-Related Fear-Dissociable Neural Sources of Different Fear Constructs

Gray Matter Abnormalities in Patients with Chronic Low Back Pain: A Systematic Review and Meta-Analysis of Voxel-Based Morphometry Studies

BACKGROUND

Numerous studies utilizing voxel-based morphometry (VBM) have documented gray matter (GM) alterations in patients with chronic low back pain (CLBP) compared to healthy controls. However, the inconsistency in GM abnormalities observed across different studies has hindered their potential application as objective neuroimaging biomarkers or therapeutic targets. To address this issue, we conducted a comprehensive meta-analysis of VBM studies to identify robust GM differences between CLBP patients and healthy controls.

METHODS

The databases including PubMed, Embase, and Web of Science were systematically searched from January 2000 to September 2022 to identify eligible neuroimaging studies. In this coordinate-based meta-analysis of VBM studies, the Seed-based d Mapping with Permutation of Subject Images method was used to quantitatively assess regional differences in GM between CLBP patients and healthy controls.

RESULTS

Thirteen VBM studies, involving a total of 574 CLBP patients and 1239 healthy controls, were included in the meta-analysis. The findings revealed that CLBP patients exhibited increased GM in the left striatum and left postcentral gyrus and decreased GM in the left superior frontal gyrus, left cerebellum, right striatum, left insula, and right middle occipital gyrus compared to healthy controls. The jackknife sensitivity analysis confirmed the robustness of these neuroimaging findings.

CONCLUSIONS

This study provides new insights into potential treatment strategies for CLBP and identifies neuroimaging biomarkers for pain chronification. These findings highlight the importance of considering regional GM abnormalities in the development of clinical interventions for CLBP.

An exploration of knowledge-organizing technologies to advance transdisciplinary back pain research

Chronic low back pain (LBP) is influenced by a broad spectrum of patient-specific factors as codified in domains of the biopsychosocial model (BSM). Operationalizing the BSM into research and clinical care is challenging because most investigators work in silos that concentrate on only one or two BSM domains. Furthermore, the expanding, multidisciplinary nature of BSM research creates practical limitations as to how individual investigators integrate current data into their processes of generating impactful hypotheses. The rapidly advancing field of artificial intelligence (AI) is providing new tools for organizing knowledge, but the practical aspects for how AI may advance LBP research and clinical are beginning to be explored. The goals of the work presented here are to: (1) explore the current capabilities of knowledge integration technologies (large language models (LLM), similarity graphs (SGs), and knowledge graphs (KGs)) to synthesize biomedical literature and depict multimodal relationships reflected in the BSM, and; (2) highlight limitations, implementation details, and future areas of research to improve performance. We demonstrate preliminary evidence that LLMs, like GPT-3, may be useful in helping scientists analyze and distinguish cLBP publications across multiple BSM domains and determine the degree to which the literature supports or contradicts emergent hypotheses. We show that SG representations and KGs enable exploring LBP's literature in novel ways, possibly providing, trans-disciplinary perspectives or insights that are currently difficult, if not infeasible to achieve. The SG approach is automated, simple, and inexpensive to execute, and thereby may be useful for early-phase literature and narrative explorations beyond one's areas of expertise. Likewise, we show that KGs can be constructed using automated pipelines, queried to provide semantic information, and analyzed to explore trans-domain linkages. The examples presented support the feasibility for LBP-tailored AI protocols to organize knowledge and support developing and refining trans-domain hypotheses.

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.

[Validation of the German version of the Fear-Avoidance Beliefs Questionnaire (FABQ-D) for shoulder disorders]

BACKGROUND

With a prevalence of up to 30%, shoulder disorders form the third largest group of musculoskeletal complaints worldwide. Their formation and development are influenced by psychosocial factors such as movement-related fear. One of the internationally most common measurements for quantifying fear of movement is the Fear-Avoidance Beliefs Questionnaire (FABQ).

OBJECTIVES

To investigate the reliability (internal consistency) and validity (structural validity, construct validity, predictive validity) of the FABQ‑D in a shoulder pain population.

MATERIALS AND METHODS

Subjects with shoulder pain were included in a multicenter cross-sectional study. In addition to fear-avoidance beliefs, pain intensity, subjective impairment in daily life and kinesiophobia were recorded. To this end, the FABQ‑D, numeric rating scale (NRS), Shoulder and Pain Disability Index (SPADI) and the Tampa Scale for Kinesiophobia (TSK-GV) were used.

RESULTS

A total of 49 subjects (24 women and 25 men) with a mean age of 41.8 years (SD = 12.8) were included. The descriptive evaluation at item level showed good internal consistency of the FABQ‑D (Cronbach's α = 0.88). The homogeneity factor differs significantly between the subscales (Loevinger's H = 0.66-0.9). The correlation analyses did not show any clear convergence of the FABQ‑D with the TSK-GV (r = 0.3501; p = 0.0137). A divergence to the constructs of the NRS (r = 0.1818; p = 0.2112) and SPADI (r = 0.4415; p = 0.0015) were confirmed. The hypothesis testing resulted in 42.87% of the assumed hypotheses and therefore a low construct validity. The FABQ‑D and the TSK-GV showed a significant influence on the duration of the complaints (R2 = 0.3652; p ≤ 0.0001). It was also shown that the greatest factors for a high FABQ‑D value were functional impairment (SPADI) and duration of symptoms (R2 = 0.3066; p = 0.0002). The subgroup analysis showed a significantly higher FABQ‑D value in older subjects (40-65 years; t = 3.8084/df = 47, p = 0.0002).

CONCLUSION

The FABQ‑D is a reliable measurement tool. The construct validity should be further investigated in future studies. This study reproduced results from previous studies in other populations. The FABQ‑D appears to be an adequate measurement tool for quantifying fear of movement in patients with shoulder disorders.

Identifying Motor Control Strategies and Their Role in Low Back Pain: A Cross-Disciplinary Approach Bridging Neurosciences With Movement Biomechanics

Persistent low back pain (LBP) is a major health issue, and its treatment remains challenging due to a lack of pathophysiological understanding. A better understanding of LBP pathophysiology has been recognized as a research priority, however research on contributing mechanisms to LBP is often limited by siloed research within different disciplines. Novel cross-disciplinary approaches are necessary to fill important knowledge gaps in LBP research. This becomes particularly apparent when considering new theories about a potential role of changes in movement behavior (motor control) in the development and persistence of LBP. First evidence points toward the existence of different motor control strategy phenotypes, which are suggested to have pain-provoking effects in some individuals driven by interactions between neuroplastic, psychological and biomechanical factors. Yet, these phenotypes and their role in LBP need further validation, which can be systematically tested using an appropriate cross-disciplinary approach. Therefore, we propose a novel approach, connecting methods from neuroscience and biomechanics research including state-of-the-art optical motion capture, musculoskeletal modeling, functional magnetic resonance imaging and assessments of psychological factors. Ultimately, this cross-disciplinary approach might lead to the identification of different motor control strategy phenotypes with the potential to translate into clinical research for better treatment options.

Back pain attitudes questionnaire: Cross-cultural adaptation to brazilian-portuguese and measurement properties

BACKGROUND

The Back Pain Attitudes Questionnaire (Back-PAQ) was developed to evaluate attitudes and beliefs of the general public, people with back pain, and healthcare professionals about the spine.

OBJECTIVES

To translate and cross-culturally adapt the Back-PAQ (34-item and 10-item versions) into Brazilian-Portuguese (Back-PAQ-Br) and test its measurement properties in a Brazilian sample.

METHODS

The cross-cultural adaptation and testing of the measurement properties followed the recommendations of international guidelines. Members of the general public, people with back pain, and healthcare professionals, for a total of 139 individuals, took part in the assessment of internal consistency, construct validity, and ceiling and floor effects. The Hospital Anxiety and Depression Scale (HADS) and the Brazilian-Portuguese version of the Tampa Scale of Kinesiophobia (TSK) were used to evaluate construct validity. Test-retest reproducibility was determined on 77 participants. Retest was performed a minimum of 1 week and a maximum of 2 weeks from the original test.

RESULTS

There was very high agreement between translators (88.2%). The Back-PAQ-Br showed excellent internal consistency (Cronbach's alpha 0.92) and excellent reproducibility (ICC 0.94; SEM 5.14 points on a 136 point scale), with a smallest detectable change (90% confidence level) of 11.93 points. There was strong correlation between Back-PAQ-Br and TSK (r = -0.72) and very weak correlation between Back-PAQ-Br and HADS (r = -0.23 for both depression and anxiety domains). No ceiling/floor effects were observed.

CONCLUSION

The translation process and cross-cultural adaptation had very high agreement between translators. The Back-PAQ-Br has excellent measurement properties that are similar to the properties of the original version.

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.

Imaging Brain Mechanisms of Functional Somatic Syndromes: Potential as a Biomarker?

When patients present with persistent bodily complaints that cannot be explained by a symptom-linked organic pathology (medically unexplained symptoms), they are diagnosed with 'functional' somatic syndromes (FSS). Despite their prevalence, the management of FSS is notoriously challenging in clinical practice. This may be because FSS are heterogeneous disorders in terms of etiopathogenesis. They include patients with primarily peripheral dysfunction, primarily centrally driven somatic symptoms, and a mix of both. Brain-imaging studies, particularly data-driven pattern recognition methods using machine learning algorithms, could provide brain-based biomarkers for these clinical conditions. In this review, we provide an overview of our brain imaging data on brain-body interactions in one of the most well-known FSS, irritable bowel syndrome (IBS), and discuss the possible development of a brain-based biomarker for FSS. Anticipation of unpredictable pain, which commonly elicits fear in FSS patients, induced increased activity in brain areas associated with hypervigilance during rectal distention and non-distention conditions in IBS. This was coupled with dysfunctional inhibitory influence of the medial prefrontal cortex (mPFC) and pregenual anterior cingulate cortex (pACC) on stress regulation systems, resulting in the activated autonomic nervous system (ANS) and neuroendocrine system stimulated by corticotropin-releasing hormone (CRH). IBS subjects with higher alexithymia, a risk factor for FSS, showed stronger activity in the insula during rectal distention but reduced subjective sensitivity. Reduced top-down regulation of the ANS and CRH system by mPFC and pACC, discordance between the insula response to stimulation and subjective sensation of pain, and stronger threat responses in hypervigilance-related areas may be a candidate brain-based biomarker.

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific sub-groups, and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test-retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter- and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.