Characterisation of motor cortex organisation in patients with different presentations of persistent low back pain

Differences in the organization of the primary motor cortex in people with and without low back pain and associations with motor control and sensory tests

Differences in organization of the primary motor cortex and altered trunk motor control (sensing, processing and motor output) have been reported in people with low back pain (LBP). Little is known to what extent these differences are related. We investigated differences in 1) organization of the primary motor cortex and 2) motor and sensory tests between people with and without LBP, and 3) investigated associations between the organization of the primary motor cortex and motor and sensory tests. We conducted a case-control study in people with (N=25) and without (N=25) LBP. The organization of the primary motor cortex (Center of Gravity (CoG) and Area of the cortical representation of trunk muscles) was assessed using neuronavigated transcranial magnetic stimulation, based on individual MRIs. Sensory tests (quantitative sensory testing, graphaesthesia, two-point discrimination threshold) and a motor test (spiral-tracking test) were assessed. Participants with LBP had a more lateral and lower location of the CoG and a higher temporal summation of pain. For all participants combined, better vibration test scores were associated with a more anterior, lateral, and lower CoG and a better two-point discrimination threshold was associated with a lower CoG. A small subset of variables showed significance. Although this aligns with the concept of altered organization of the primary motor cortex in LBP, there is no strong evidence of the association between altered organization of the primary motor cortex and motor and sensory test performance in LBP. Focusing on subgroup analyses regarding pain duration can be a topic for future research.

BACK-to-MOVE: Machine learning and computer vision model automating clinical classification of non-specific low back pain for personalised management

BACKGROUND

Low back pain (LBP) is a major global disability contributor with profound health and socio-economic implications. The predominant form is non-specific LBP (NSLBP), lacking treatable pathology. Active physical interventions tailored to individual needs and capabilities are crucial for its management. However, the intricate nature of NSLBP and complexity of clinical classification systems necessitating extensive clinical training, hinder customised treatment access. Recent advancements in machine learning and computer vision demonstrate promise in characterising NSLBP altered movement patters through wearable sensors and optical motion capture. This study aimed to develop and evaluate a machine learning model (i.e., 'BACK-to-MOVE') for NSLBP classification trained with expert clinical classification, spinal motion data from a standard video alongside patient-reported outcome measures (PROMs).

METHODS

Synchronised video and three-dimensional (3D) motion data was collected during forward spinal flexion from 83 NSLBP patients. Two physiotherapists independently classified them as motor control impairment (MCI) or movement impairment (MI), with conflicts resolved by a third expert. The Convolutional Neural Networks (CNNs) architecture, HigherHRNet, was chosen for effective pose estimation from video data. The model was validated against 3D motion data (subset of 62) and trained on the freely available MS-COCO dataset for feature extraction. The Back-to-Move classifier underwent fine-tuning through feed-forward neural networks using labelled examples from the training dataset. Evaluation utilised 5-fold cross-validation to assess accuracy, specificity, sensitivity, and F1 measure.

RESULTS

Pose estimation's Mean Square Error of 0.35 degrees against 3D motion data demonstrated strong criterion validity. Back-to-Move proficiently differentiated MI and MCI classes, yielding 93.98% accuracy, 96.49% sensitivity (MI detection), 88.46% specificity (MCI detection), and an F1 measure of .957. Incorporating PROMs curtailed classifier performance (accuracy: 68.67%, sensitivity: 91.23%, specificity: 18.52%, F1: .800).

CONCLUSION

This study is the first to demonstrate automated clinical classification of NSLBP using computer vision and machine learning with standard video data, achieving accuracy comparable to expert consensus. Automated classification of NSLBP based on altered movement patters video-recorded during routine clinical examination could expedite personalised NSLBP rehabilitation management, circumventing existing healthcare constraints. This advancement holds significant promise for patients and healthcare services alike.

What Do We Know about Nociplastic Pain?

Nociplastic pain is a recently distinguished type of pain, distinct from neuropathic and nociceptive pain, and is well described in the literature. It is often mistaken for central sensitization. Pathophysiology has not been clearly established with regard to alteration of the concentration of spinal fluid elements, the structure of the white and gray matter of the brain, and psychological aspects. Many different diagnostic tools, i.e., the painDETECT and Douleur Neuropathique 4 questionnaires, have been developed to diagnose neuropathic pain, but they can also be applied for nociplastic pain; however, more standardized instruments are still needed in order to assess its occurrence and clinical presentation. Numerous studies have shown that nociplastic pain is present in many different diseases such as fibromyalgia, complex regional pain syndrome type 1, and irritable bowel syndrome. Current pharmacological and nonpharmacological treatments for nociceptive and neuropathic pain are not entirely suitable for treating nociplastic pain. There is an ongoing effort to establish the most efficient way to manage it. The significance of this field has led to several clinical trials being carried out in a short time. The aim of this narrative review was to discuss the currently available evidence on pathophysiology, associated diseases, treatment possibilities, and clinical trials. It is important that physicians widely discuss and acknowledge this relatively new concept in order to provide optimized pain control for patients.

The effect of skilled motor training on corticomotor control of back muscles in different presentations of low back pain

Transcranial magnetic stimulation (TMS) has revealed differences in the motor cortex (M1) between people with and without low back pain (LBP). There is potential to reverse these changes using motor skill training, but it remains unclear whether changes can be induced in people with LBP or whether this differs between LBP presentations. This study (1) compared TMS measures of M1 (single and paired-pulse) and performance of a motor task (lumbopelvic tilting) between individuals with LBP of predominant nociceptive (n = 9) or nociplastic presentation (n = 9) and pain-free individuals (n = 16); (2) compared these measures pre- and post-training; and (3) explored correlations between TMS measures, motor performance, and clinical features. TMS measures did not differ between groups at baseline. The nociplastic group undershot the target in the motor task. Despite improved motor performance for all groups, only MEP amplitudes increased across the recruitment curve and only for the pain-free and nociplastic groups. TMS measures did not correlate with motor performance or clinical features. Some elements of motor task performance and changes in corticomotor excitability differed between LBP groups. Absence of changes in intra-cortical TMS measures suggests regions other than M1 are likely to be involved in skill learning of back muscles.

In the back of your mind: Cortical mapping of paraspinal afferent inputs

Topographic organisation is a hallmark of vertebrate cortex architecture, characterised by ordered projections of the body's sensory surfaces onto brain systems. High-resolution functional magnetic resonance imaging (fMRI) has proven itself as a valuable tool to investigate the cortical landscape and its (mal-)adaptive plasticity with respect to various body part representations, in particular extremities such as the hand and fingers. Less is known, however, about the cortical representation of the human back. We therefore validated a novel, MRI-compatible method of mapping cortical representations of sensory afferents of the back, using vibrotactile stimulation at varying frequencies and paraspinal locations, in conjunction with fMRI. We expected high-frequency stimulation to be associated with differential neuronal activity in the primary somatosensory cortex (S1) compared with low-frequency stimulation and that somatosensory representations would differ across the thoracolumbar axis. We found significant differences between neural representations of high-frequency and low-frequency stimulation and between representations of thoracic and lumbar paraspinal locations, in several bilateral S1 sub-regions, and in regions of the primary motor cortex (M1). High-frequency stimulation preferentially activated Brodmann Area (BA) regions BA3a and BA4p, whereas low-frequency stimulation was more encoded in BA3b and BA4a. Moreover, we found clear topographic differences in S1 for representations of the upper and lower back during high-frequency stimulation. We present the first neurobiological validation of a method for establishing detailed cortical maps of the human back, which might serve as a novel tool to evaluate the pathological significance of neuroplastic changes in clinical conditions such as chronic low back pain.