A functional Magnetic Resonance Imaging study of patients with Polar Type II/III complex shoulder instability

Abstract

The pathophysiology of Stanmore Classification Polar type II/III shoulder instability is not well understood. Functional Magnetic Resonance Imaging was used to measure brain activity in response to forward flexion and abduction in 16 patients with Polar Type II/III shoulder instability and 16 age-matched controls. When a cluster level correction was applied patients showed significantly greater brain activity than controls in primary motor cortex (BA4), supramarginal gyrus (BA40), inferior frontal gyrus (BA44), precentral gyrus (BA6) and middle frontal gyrus (BA6): the latter region is considered premotor cortex. Using voxel level correction within these five regions a unique activation was found in the primary motor cortex (BA4) at MNI coordinates -38 -26 56. Activation was greater in controls compared to patients in the parahippocampal gyrus (BA27) and perirhinal cortex (BA36). These findings show, for the first time, neural differences in patients with complex shoulder instability, and suggest that patients are in some sense working harder or differently to maintain shoulder stability, with brain activity similar to early stage motor sequence learning. It will help to understand the condition, design better therapies and improve treatment of this group; avoiding the common clinical misconception that their recurrent shoulder dislocations are a form of attention-seeking.

Shoulder-Pacemaker Treatment Concept for Posterior Positional Functional Shoulder Instability: A Prospective Clinical Trial

“… 1 , 18 , 37 Furthermore, a functional magnetic resonance imaging (MRI) study revealed increased activity within the primary motor cortex, supramarginal gyrus, inferior frontal gyrus, premotor cortex, and middle frontal gyrus during shoulder movement in patients with FSI, indicating increased neural activity similar to early learning of a motor sequence. 15 Affected patients experience various symptoms, including chronic pain, movement restriction attributed to weakness or blockage, and a persistent feeling of shoulder instability. 28 , 29 Extreme limitations during daily activities and sports, as well as “bizarre-looking” dislocations, can lead to stigmatization among peers and emotional stress of the affected patients.…”

Neuroplasticity Caused by Peripheral Proprioceptive Deficits

“…S2, http://links.lww.com/MSS/C418 ), contralateral S1, and secondary sensorimotor cortex were central processing sites for proprioceptive information ( 19 ). Howard et al ( 13 ) demonstrated that patients with complex shoulder instability showed significantly greater brain activity than controls in M1, supramarginal gyrus, inferior frontal gyrus, and premotor cortex using fMRI during an active shoulder motion task. Moreover, passive and voluntary movements induced activation in the same parts of the cerebellar hemispheres and dentate nuclei ( 19 ).…”

“…Compared with the traditional joint position-matching task, the passive motion task/fMRI method can obtain pure brain activation, which is evoked by proprioceptive stimulation, whereas the traditional joint position-matching task includes many other neural processes, such as attention, consciousness, and memory for reproduction of joint position matching. Using the fMRI method for patients with RSI, previous studies elucidated brain activity related to shoulder apprehension ( 12 ) and abnormal brain activity during active shoulder flexion and abduction ( 13 ).…”

Chronic musculoskeletal impairment is associated with alterations in brain regions responsible for the production and perception of movement

“…fMRI data were aligned with T1‐weighted images using FMRIB's linear image registration tool (FLIRT) optimised with boundary‐based registration (Howard et al . 2019). Non‐linear transformations to MNI152 standard space were used to transform fMRI data into a common space.…”

“…All fMRI data were subject to manual independent components analysis (ICA) denoising prior to further analysis (Hétu et al 2013). fMRI data were aligned with T1-weighted images using FMRIB's linear image registration tool (FLIRT) optimised with boundary-based registration (Howard et al 2019). Non-linear transformations to MNI152 standard space were used to transform fMRI data into a common space.…”