Thesis
Reference
L'origine cérébrale de l'appréhension dans l'instabilité gléno-humérale
CUNNINGHAM, Gregory
Abstract
L'appréhension est le signe clinique le plus fréquemment rencontré dans l'instabilité gléno-humérale traumatique, et peut persister en postopératoire malgré une épaule cliniquement stable. Partant de l'hypothèse qu'il existe un substrat neurologique à ce phénomène qui peut être mis en évidence par la neuro-imagerie fonctionnelle, nous avons investigué une cohorte de patients victimes d'appréhension par IRM fonctionnelle cérébrale.
Nous avons mis en évidence chez ces sujets des altérations de connectivité cérébrale majeures et complexes, que nous avons dans un deuxième temps corrélé à des scores cliniques subjectifs fréquemment utilisés en chirurgie de l'épaule. Ces résultats sont présentés dans les deux articles qui font l'objet de cette thèse.
CUNNINGHAM, Gregory. L'origine cérébrale de l'appréhension dans l'instabilité gléno-humérale. Thèse de doctorat : Univ. Genève, 2015, no. Méd. 10783
URN : urn:nbn:ch:unige-801495
DOI : 10.13097/archive-ouverte/unige:80149
Available at:
http://archive-ouverte.unige.ch/unige:80149
Disclaimer: layout of this document may differ from the published version.
1 / 1
Section de médecine Clinique Département de Chirurgie
Service de Chirurgie Orthopédique et Traumatologie de l’appareil moteur
Thèse préparée sous la direction du Professeur Pierre HOFFMEYER et du Privat-docent Alexandre LÄDERMANN
" L’origine cérébrale de l’appréhension dans l’instabilité gléno-humérale "
Thèse
présentée à la Faculté de Médecine de l'Université de Genève
pour obtenir le grade de Docteur en médecine par
Gregory James CUNNINGHAM de
Wohlen (AG)
Thèse n° 10783 Genève
2015
1 TABLE DES MATIÈRES
Abréviations………... Page 2
Remerciements………. Page 3
Conflits d’intérêt………. Page 3
Introduction……… Page 4 Article 1 : Shoulder apprehension impacts large-scale functionnal brain networks
Introduction……… Page 7 Materials and Methods……… Page 8
Results……… Page 11
Discussion……….. Page 13
Strenghts and Limitations……… Page 15
Conclusions……… Page 16
Figures and Legends……… Page 17
References………. Page 21
Article 2 : Neural correlates of clinical scores in patients with shoulder apprehension
Introduction……… Page 24 Materials and Methods………. Page 25
Results……… Page 29
Discussion……….. Page 30
Limitations……….. Page 34
Conclusions……… Page 34
Figures, Tables and Legends………. Page 35
References………. Page 39
Discussion de la Thèse…………...……….. Page 43
Références………. Page 48
2 ABRÉVIATIONS
o dACC=dorsal anterior cingulate cortex o dlPFC=dorsolateral prefrontal cortex o dmPFC=dorsomedial prefrontal cortex o DTI = Diffusion Tensor Imaging
o FDR = False Discovery Rate o GLM=general linear model o IC = independent component
o ICA = Independent Component Analyses o pVAS = Pain Visual Analog Scale o Rowe = Rowe score for instability o SST = Simple Shoulder Test o SSV = Subjective Shoulder Value o TBSS = tract-based spatial statistics
o TICA = tensorial independent component analysis o VBM = voxel-based morphometry
o WOSI = Western Ontario Shoulder Instability
3 REMERCIEMENTS
Je souhaite tout d’abord exprimer toute ma gratitude envers mon maître, le Professeur Hoffmeyer, qui a eu l’idée originale de ce projet et qui m’a donné l’opportunité de pouvoir développer mon intérêt pour la chirurgie de l’épaule et du coude.
Je remercie également mon second mentor et mon ami, le Docteur Alexandre Lädermann, pour m’avoir toujours soutenu au long de mon parcours.
Merci au Docteur Sven Haller et Davide Zanchi pour leur grande contribution à ce projet ayant mené à nos deux premières publications qui font l’objet de cette thèse.
Finalement, un grand merci à mes parents, Elsbeth et Tim, ainsi que ma femme, Sarah, pour leur présence et leur soutien.
CONFLITS D’INTERETS
L’auteur et les co-auteurs de cette thèse, ainsi que leurs familles immédiates, n’ont reçu aucun soutien financier ou autres bénéfices d’un tiers commercial relié au sujet de cette thèse.
4 INTRODUCTION
Au cours de l’évolution de l’être humain de sa station quadrupède à bipède, l’articulation gléno-humérale a connu des changements de configuration drastiques.
Elle est devenue l’articulation la plus mobile du corps avec une antériorisation des amplitudes articulaires afin de permettre à l’être humain de manœuvrer dans son champ visuel. Ce processus s’est développé au détriment de la stabilité et explique respectivement pourquoi l’instabilité gléno-humérale est si fréquente, touchant près de 2% de la population générale, et pourquoi 97% des luxations sont antérieures. 1,2 L’incidence de ce phénomène a été estimé dans un centre urbain européen à 40/100’000 personnes-année.3 Cela signifie que chaque année dans une ville telle que Genève, jusqu’à 350 individus présenteront un premier épisode de luxation gléno-humérale. Parmi ces derniers, jusqu’à 96% pourront présenter une instabilité récurrente,3 dont le risque est principalement lié au jeune âge, la pratique de sports de contact, ainsi qu’à l’étendue des pertes osseuses consécutives au traumatisme.4 La plupart de ces personnes nécessiteront une stabilisation chirurgicale, et la question de la légitimité à stabiliser ces patients à risque, d’emblée après un premier épisode de luxation, suscite encore de vifs débats.5-7 L’impact socio-économique de ce phénomène est important, tant au niveau des frais médico-soignants qu’au niveau de l’absentéisme professionnel engendré, vu que c’est une population jeune et active qui est la plus touchée.
L’appréhension est le signe clinique le plus sensible8 et le plus fréquemment rapporté par les patients victimes d’instabilité gléno-humérale antéro-inférieure. Elle se définit par une sensation de luxation imminente lorsque le bras est amené en abduction et en rotation externe. La précocité de survenue de cette sensation lors de la trajectoire du bras semble particulièrement corrélée à la perte osseuse au niveau du rebord antérieur de la glène.9
Le taux d’échec après stabilisation chirurgicale de l’épaule se situe, dans la littérature, autour des 10% mais peut varier entre 2 et 70%.10,11 La disparité de ces chiffres peut être expliquée par des indications ou techniques chirurgicales inadéquates menant à de mauvais résultats, ainsi que le manque de consensus dans la définition de ce qui est une récidive, certains auteurs ne considérant pas, par exemple, une sub-luxation anamnestique comme une véritable récurrence.
5
Par ailleurs, certains patients opérés gardent une appréhension positive en l’absence de récidive de luxation ou de sub-luxation, et ce malgré une épaule jugée cliniquement stable. L’origine de cette appréhension persistante reste peu claire et peut être théoriquement liée soit à une instabilité mécanique sous forme de micromouvements non-détectables, entité appelée épaule douloureuse et instable (« unstable painful shoulder »),12 ou encore de « redondance capsulaire antérieure »13 soit à une dysfonction proprioceptive causée par des lésions nerveuses périphériques dues aux luxations, ou finalement à une séquelle cérébrale consécutive à l’apprentissage d’un stimulus négatif.
L’instabilité d’épaule est une entité dont la description remonte à plus de 5000 ans, les premières traces étant des gravures sur le mur d’un tombeau égyptien. Puis, au fil des siècles, plusieurs auteurs la décrivent, dont Hippocrate, qui propose une technique de réduction et de stabilisation par application d’un fer chaud dans le creux axillaire. L’avènement de la radiologie avec dans un premier temps la radiographie conventionnelle entre les années ’20 et ’40 sur lesquelles sont respectivement décrites les lésions de Bankart et de Hill-Sachs, puis l’imagerie moderne et l’arthroscopie dans les années ’80, ont permis d’approfondir dans un grand niveau de détail les mécanismes derrière l’instabilité gléno-humérale.
Or, si l’épaule de ces patients instables a été interrogée avec tant de détail, personne n’avait encore interrogé leur cerveau qui est pourtant le siège de la sensation d’instabilité. Nous avons donc entrepris cette étape, partant de l’hypothèse que l’appréhension était consécutive à un remodelage cérébral plutôt qu’à la seule persistance d’une instabilité au niveau de l’articulation gléno-humérale.
Après avoir obtenu l’accord de la commission d’éthique en 2011, nous avons mené une recherche prospective, incluant tout patient victime d’instabilité gléno-humérale antérieure traumatique se présentant à notre consultation spécialisée de l’épaule au sein du service de chirurgie orthopédique en vue d’une indication opératoire. Ces patients furent soumis à une IRM fonctionnelle (IRMf) cérébrale, pendant laquelle l’appréhension fut visuellement suggérée par des séquences d’animation de situations mettant l’épaule à risque de luxation (smash au volley-ball, lancer de javelot, etc.). L’imagerie IRMf préopératoire fut comparée à celle d’un groupe
6
contrôle de volontaires sains, et les résultats furent interprétés par trois neuro- radiologues (S.H., D.v.d.V. et D.Z.).
Ce projet de recherche a abouti à 2 publications qui font l’objet de cette thèse : Shoulder apprehension impacts large-scale functionnal brain networks Haller S, Cunningham G, Laedermann A, Hofmeister J, Van De Ville D, Lovblad KO, Hoffmeyer P.
Am J Neuroradiol. 2014 Apr;35(4):691-7
Neural correlates of clinical scores in patients with shoulder apprehension Cunningham G, Zanchi D, Emmert K, Kopel R, Van De Ville D, Lädermann A, Haller S, Hoffmeyer P.
Med Sci Sports Exerc. 2015 Dec;47(12):2612-20
7
Article 1: Shoulder Apprehension Impacts Large-Scale Functional Brain Networks
INTRODUCTION
Shoulder apprehension is defined as anxiety and resistance in patients with a history of anterior glenohumeral instability. The apprehension sign is a physical finding in which placement of the humerus in the position of abduction to 90° and maximal external rotation produces anxiety and resistance in patients with a history of anterior glenohumeral instability.1,2
Despite the clearly established clinical findings of shoulder apprehension, the neuronal mechanisms associated with this subjective perception of anxiety and resistance remain unexplored. In patients with recurrent complaints of persisting apprehension after surgical stabilization, it is often difficult to diagnose and appropriately address the underlying problem.
Although a bony defect has been recognized as a major cause of residual instability,3 some patients experience apprehension without any proven re- current dislocation. It is not clear whether the origin of the com- plaint is true recurrent instability or whether it stems from a cerebral pattern linking a certain movement or position to a subjective sensation of apprehension. This type of apprehension could, in some cases, arise from a previously memorized unpleasant sensation associated with a particular movement or position leading to a protective reflex action, rather than being the result of true persisting instability. Failure to recognize and adequately ad- dress this issue of persisting apprehension because of cerebral patterning may result in poor outcomes and even lead to unnecessary revision surgery.
To specifically probe the neuronal activations associated with shoulder apprehension, we developed animation videos illustrating typical movements of shoulder apprehension and matched control videos without apprehension movements. These videos were shown during fMRI to a carefully selected group of patients with shoulder apprehension and matched healthy volunteers. Functional connectivity as well as structural changes in gray and white matter was assessed. Specifically, we addressed the hypothesis that the visual presentation of apprehension-related videos induces patterns of functional connectivity in brain networks associated with motor resistance and anxiety.
8 MATERIALS AND METHODS
Participants
The local institutional ethical committee approved this prospective study, and all participants gave written informed consent be- fore inclusion. We included 15 consecutive right-handed male patients with right-sided (n=9) or left-sided (n=6) glenohumeral instability and positive shoulder apprehension test (27.5 ± 6.4 years), who were recruited during consultation by the same shoulder surgeon (A.L., 13 years of experience). They all underwent an fMRI examination before surgical shoulder stabilization by this same surgeon. Ten healthy male right-handed control-matched participants were randomly selected from the general population (29.0 ± 4.7 years, no significant difference in mean age between groups). The exclusion criteria for control patients was any his- tory of shoulder injury or instability as well as hyperlaxity, defined as more than 85° of elbow-to-waist external rotation. All participants had normal or corrected-to-normal visual acuity, and none reported a history of major medical disorders (cancer, cardiac illness), sustained head injury, psychiatric or neurologic disorders, or alcohol or drug abuse. Participants who used psycho- tropics, stimulants, and B-blockers on a regular basis were excluded.
fMRI Task
The fMRI task consisted of a block-design of 2 active conditions and 1 rest condition. In the active APPREHENSION condition, self-made animation movies (10 seconds) were visually presented, including daily activities such as putting the right shoulder at risk for anteroinferior dislocation and hence triggering apprehension, for example, arming the shoulder with a javelin, quickly reaching backwards for a seatbelt, and so forth (created by C.G., 3 years of experience). The videos for the CONTROL condition presented an identical situation except for the lack of suggestive movement, which induces apprehension. After each movie, a visual analog scale was presented for 2.5 seconds, and participants rated the degree of apprehension by using a MR-compatible response box, followed by a rest period consisting of the visual presentation of a fixation cross for 17.5 seconds. The 9-point visual rating scale ranged from very unpleasant (+1) to neutral (0) to pleasant (-1). Each run consisted of 6 active and 6 control videos presented in a pseudorandomized fashion. With inclusion of the additional fixation cross-phase, each run lasted 370 seconds with each participant performing 2 runs. Before fMRI scanning, participants were familiarized with the task by using a training program outside of the fMRI scanner.
9 MR Imaging
MR imaging was performed on a clinical routine whole-body 3T MR scanner (Trio; Siemens, Erlangen, Germany). Functional im- aging implemented a standard EPI sequence with the following fundamental parameters: 1) whole-brain coverage, 96 x 96 matrix, 39 sections, voxel size 2.3 x 2.3 x 3.3 mm3, TE of 30 ms, TR of 2500 ms, 148 repetitions; 2) a 3D T1 sequence with the following fundamental parameters: 256 x 256 matrix, 176 sections, 1 x 1 x 1mm3, TE of 2.3ms, TR of 2300ms; and 3) a DTI sequence with the following fundamental parameters: 30 diffusion directions b=1000 s/mm2 isotropically distributed on a sphere, 1 reference b=0 s/mm2 image with no diffusion weighting, 128 x 128 x 64matrix,2 x 2 x 2mm voxelsize, TE of 92 ms, TR of 9000 ms, and 1 average.
Statistical Analysis
Statistical analysis was performed in GraphPad Prism Version 5.0 (GraphPad Software, San Diego, California; behavioral data), FSL Version 5.0.2.1 (http://fsl.fmrib.ox.ac.uk; tensorial independent component analysis [TICA], voxel-based morphometry [VBM], and tract-based spatial statistics [TBSS]), and Matlab Version R2012b (MathWorks, Natick, Massachusetts;
correlation analyses) by S.H. (12 years of experience) and D.v.d.V. (14 years of experience).
Analysis of Behavioral Data
After normality testing (D’Agostino-Pearson omnibus test), the participant’s age was analyzed by use of a 2-sample t test. The behavioral responses for APPREHENSION vs CONTROL for patients and control participants were analyzed by use of group-level ANOVA followed by pair-wise Bonferroni multiple comparison tests.
TICA Analysis of Functional Connectivity
Analysis was carried out by TICA4 as implemented in MELODIC (Multivariate Exploratory Linear Decomposition into Indepen- dent Components) Version 3.10, part of FSL. The following pre-processing was applied: 1) masking of nonbrain voxels, 2) voxel- wise de- meaning of the data, and 3) normalization of the voxelwise variance. Preprocessed data were whitened and projected into a 30-dimensional subspace by use of the principal component analysis (30 components by use of automatic component estimation in FSL). The whitened observations were decomposed into sets of vectors, which describe signal variation across the temporal domain (time courses), the session/participant do- main, and across the spatial domain (maps) by optimization for non-Gaussian spatial source distributions by a
10
fixed-point iteration technique.5 Estimated component maps were divided by the standard deviation of the residual noise and thresholded by fitting a mixture model to the histogram of intensity values.6 Three non- neurologic noise components (visual inspection, and pseudoactivation of the brain surface or vascular system) were excluded from further processing. For each of the 27 remaining independent components (ICs), the associated Smodes (which are measures of the activation strength of the component) were post hoc compared between APPREHENSION vs CONTROL by implementation of 2-sample t tests and Bonferroni correction for multiple comparisons. The analysis was preformed 3 times: 1) right shoul- der patients vs control participants, 2) left shoulder patients vs control participants, and 3) all shoulder patients vs control participants. From the 3 remaining ICs, we also post hoc identified 2 of these ICs as task positive (ICs 12 and 17), and 1 as task negative (IC 30), and correlated the combined Smodes (ie, average of Smodes of ICs 12 and 17 - Smode of IC 30) with the average behavioral responses by use of Spearman rho. The correlation coefficient was statistically evaluated by use of 2-tailed nonparametric permutation testing. It should be noted that the spatial maps of ICs 12 and 17 also contain
“negative” regions (ie, dorsal anterior cingulate cortex [dACC]) that contribute as task negative.
General Linear Model Analysis of Task-Related Activation
Task-related general linear model (GLM) data processing was carried out by use of FEAT (fMRI Expert Analysis Tool) Version 5.98, part of FSL. At the first level, the contrast of APPREHENSION vs CONTROL (and the inverse comparison) was calculated separately for each run of each participant. At the second level, the intraparticipant difference in the 2 runs of APPREHENSION vs CONTROL (and the inverse comparison) was assessed individually.
At the third level, the group difference between all 15 patients and 10 control participants was calculated. We carried out higher- level analysis by using a fixed-effects model, forcing the random- effects variance to zero in FLAME (FMRIB Local Analysis of Mixed Effects).7-9 Z (Gaussianized T/F)-statistic images were thresholded by use of clusters determined by Z >
2.3 and a corrected cluster significance threshold of P = .05.
Gray Matter VBM Analysis of T1 Data
The VBM analysis was analyzed by use of the FSL software package (Version 5.0.2.1).
Standard processing steps were used, as described previously.10,11 The essential processing steps included brain extraction in BET (Brain Extraction Tool, part of FSL), tis- sue-type segmentation by FAST4 (part of FSL), nonlinear transformation into Montreal
11
Neurological Institute reference space, and creation of a study-specific GM template. The native GM images were then nonlinearly re-registered to this template. The modulated segmented images were then smoothed with an isotropic Gaussian kernel with a sigma of 2 mm. Finally, we applied voxelwise GLM by using permutation-based nonparametric testing (Randomise, part of FSL), correcting for multiple comparisons implementing threshold-free cluster enhancement.12 Fully corrected P values < .05 are considered as significant. Similar to the TICA analysis above, this analysis was repeated for right shoulder patients vs control participants, left shoulder patients vs control participants, and all shoulder patients vs control participants.
White Matter TBSS Analysis of DTI Data
The TBSS analysis of the DTI data was again done implementing the FSL software package (Version 5.0.2.1), according to the standard procedure described in detail.13 In principle, TBSS projects all participants' fractional anisotropy data onto a mean fractional anisotropy tract skeleton by using nonlinear registration. The tract skeleton is the basis for voxelwise cross-participant statistics and reduces potential misregistrations as the source for false- positive or false-negative results. Equivalent to the VBM analysis discussed above, we performed voxelwise statistical analysis with threshold-free cluster enhancement12 correction for multiple comparisons, considering fully corrected P values < .05 as significant. Again, analysis was repeated for right shoulder patients vs control participants, left shoulder patients vs control participants, and all shoulder patients vs control participants.
RESULTS Behavioral Data
In both patients and healthy volunteers, the APPREHENSION videos induced significantly increased (P < .001 corrected patients; P < .01 corrected control volunteers) unpleasant ratings compared with CONTROL videos. Moreover, when we compared patients vs healthy control participants, only the APPREHENSION videos (P < .01 corrected), but not the CONTROL videos, yielded more unpleasant ratings in patients vs control volunteers (Fig 1).
These results confirm that our experimental setup induces subjective perception of unpleasantness associated with the visual perception of our shoulder apprehension movies in both patients and control participants. It also demonstrates that patients had significantly more unpleasant ratings for APPREHENSION videos vs CONTROL videos compared with matched-control participants.
12 Functional Connectivity fMRI Activations
The independent component analysis yielded 3 task-related ICs with a significant (P < .05 corrected) difference in Smode (a measure of the activation strength of the ICs) for patients compared with control participants. All 3 ICs were significant for all patients vs control participants and right shoulder patients vs control participants. In addition, IC 17 was significant for left shoulder patients vs control participants, whereas the other 2 components showed a clear equivalent but just a nonsignificant trend. It is noteworthy that there was a higher number of right vs left shoulder patients, which explains this higher level of significance. There was no significant difference between right vs left shoulder patients.
Therefore, we report the following results for all patients vs control participants.
Patients vs control participants had a significantly (P < .05 corrected) higher functional connectivity in 2 almost-mirror symmetric components, notably in the bilateral primary sensory-motor area and dorsolateral prefrontal cortex (dlPFC), bilateral dorsomedial prefrontal cortex (dmPFC), anterior insula, and dACC (+148% SMode in right hemisphere IC 12, and +144% Smode in left hemisphere IC 17). In contrast, patients had significantly reduced functional connectivity in a bilateral higher-level visual network including the parietal region (−185% Smode IC 30) (Fig 2).
The additionally performed correlation between task-positive minus task-negative Smodes and behavioral ratings revealed a significant negative correlation (rho = −0.47, P = .02) for all participants, and trends within the populations (rho = −0.63, P = .05 in control participants and rho = −0.31, P = .27 in patients).
These correlations indicate increasing functional connectivity activation strength in task- positive networks with increasing unpleasantness (Fig 3).
GLM Analysis of Task-Related Activation
The task-related GLM analysis revealed activation in the left primary sensory-motor area and dlPFC, which overlaps with IC 17, yet at a lower degree of significance. The corresponding contralateral regions showed a clear trend, which remained just below the multiple comparisons corrected threshold (Fig 4).
VBM Analysis of Gray Matter and TBSS Analysis of White Matter
The VBM analysis of GM as well as the TBSS analysis of WM revealed no differences between groups.
13 DISCUSSION
Patients with shoulder apprehension have increased functional connectivity in the primary sensory-motor areas compatible with motor resistance, dlPFC associated with cognitive control of motor behavior, and the dACC/dmPFC and anterior insula associated with anxiety and emotional regulation, despite the absence of potentially confounding structural alterations.
The current investigation is based on the observation that functional connectivity is not stationary but is variable with time. Functional connectivity may change spontaneously,14 by exogenous stimulation,15 or by learning.16-18 Therefore, we exposed participants to videos of situations, which typically induce apprehension, to modulate functional connectivity related to the perception of apprehension. Our approach thus differs from “classic” resting-state fMRI studies without any specific task.19,20 It is noteworthy that this change in paradigm is essential for the current investigation, as we only expect subtle changes at baseline in “classic”
resting-state fMRI of apprehension patients who have no cognitive impairments. The presentation of apprehension videos is thus necessary to induce functional connectivity associated with the perception and, more generally, the processing of shoulder apprehension.
The functional connectivity was increased in patients vs volunteers by approximately 145% in the bilateral primary motor and sensory areas, as well as the bilateral dlPFC. It is interesting to note that during the shoulder apprehension test,1,2 patients had an increased muscle tone and resistance in response to external rotation of the shoulder. Our findings of increased functional connectivity in the primary sensory-motor area as well as in the dlPFC, which is consistently involved in the cognitive control of motor behavior,21 are consistent with a preparation or readiness activation of the motor system in muscular resistance. Moreover, Hamilton et al22 and Korgaonkar et al23 propose the dlPFC is involved in the appraisal of negative emotional inputs (appraisal being deregulated in major depressive disorder). In the context of our shoulder apprehension videos, the increase in dlPFC might indicate an increased reappraisal of negative information related to the negative valence of the videos, in addition to its role in modulating pre/motor regions.
In addition, we observed an increase in functional connectivity in the dmPFC, dACC, and anterior insula. These regions are consistently involved in emotional regulation and anxiety.
Although no previous imaging study specifically addressed apprehension, we consider anxiety and fear as key cognitive processes associated with shoulder apprehension. A recent meta-analysis of instructed fear studies concludes that the dACC/dmPFC is a part of a “core”
fear network, which is activated irrespective of how fear was learned.24 One study assessed
14
anticipatory anxiety in participants with spider phobia25 and identified increased activation notably in the dACC/dmPFC and insula in spider phobics compared with volunteers while anticipating phobic stimulation. Another study assessed anticipation of interoceptive threat in 15 participants reporting high vs 14 participants reporting low fear.26 Participants were trained that 1 of 2 cues predicted the occurrence of a hyperventilation task, which reliably produced body symptoms in all participants. The comparison of high-fear vs low-fear groups during the anticipation period, again, activated the dACC/dmPFC and bilateral anterior insula.
Moreover, anticipatory anxiety was assessed in 14 healthy volunteers.27 The paradigm consisted of a visual presentation of blue circles associated with a certain likelihood of aversive transcutaneous electrical nerve stimulations vs visual presentation of red circles without painful stimuli. Again, anticipatory anxiety was associated with activations in the bilateral anterior insula and midline frontal cortex (overlapping with the dACC/dmPFC).
Spider phobia, hyperventilation, and transcutaneous electrical nerve stimulations as aversive stimuli evidently differ from shoulder apprehension in our current investigation. Nevertheless, the anticipatory anxiety in the discussed studies is an essential cognitive process involved in shoulder apprehension. In a consistent fashion, the reported activations of these above- mentioned studies remarkably overlap with parts of the observed upregulated networks of our current investigation. This finding is remarkable, as the above-mentioned fMRI studies used hypothesis-driven data analyses, whereas our current investigation implemented a hypothesis-free independent component analysis. Only 1 previous study assessed functional connectivity in anticipatory anxiety; however, it implemented a fundamentally different region- of-interest approach.28 This study included 14 anxiety-positive and 14 anxiety-normative participants performing an affective picture anticipation task. In a first step, activation in the bilateral anterior insula was identified in a task-related analysis, replicating the results of the task-related anticipatory anxiety studies discussed above. In a second step, functional connectivity was calculated with the left and right anterior insula as a seed region—in contrast to the exploratory independent component analysis of our current study, which assessed the entire brain without predefined seed regions. Nevertheless, the bilateral anterior insula in the anxiety-positive group overlap considerably with the networks identified in our study.
It is worthwhile emphasizing that the alterations in functional connectivity in the current investigation were identified by use of data-driven IC analysis without prior assumptions.
Nevertheless, the observed changes in functional connectivity are by no means random yet very meaningful in the context of shoulder apprehension, as discussed above. The relevance of these task-correlated networks is further supported by the additionally performed hypothesis-driven GLM analysis, which demonstrated overlapping activations in the left
15
sensory-motor areas and dlPFC as well as a clear and almost significant trend in the contralateral areas. That the functional-connectivity TICA analysis is more sensitive compared with the task-related GLM analysis can be explained by the fact that GLM requires strict multiple-comparisons correction of approximately 100,000 voxels instead of only 30 ICs. In addition, TICA averages signal across multiple voxels, thereby increasing the signal- to-noise ratio compared with the single-voxel GLM analysis.
Moreover, the significant correlation between ratings and functional activation strengths revealed that task-positive networks, including the sensory-motor area, dlPFC, and dACC/dmPFC, are activated more strongly during the processing of unpleasant experiences.
In contrast, the task-negative higher-level visual networks are less activated in subsequent resting blocks. Therefore, shoulder apprehension progressively disturbs the balance between task-positive and task-negative networks as unpleasantness increases. Correspondingly, delayed recovery of resting-state activity in regions of the default mode network has also been reported after exposure to unpleasant visual movie fragments29 or after a demanding cognitive task such as regulation by use of real-time fMRI neurofeedback.30 These findings not only show the direct implication of shoulder apprehension and the motor networks but also show brain processes likely to be related to interoceptive awareness, anxiety, and emotional regulation.
STRENGHTS AND LIMITATIONS
Strengths of our current investigation are the strict patient selection, the complementary analyses of functional connectivity and task-related fMRI, and the exclusion of potentially confounding morphometric changes in both GM and WM. However, several limitations should be considered. First, the study group was small and was limited to men. Second, to increase the number of patients, we grouped together those with left and right shoulder instability, as we assumed that the cognitive processes related to apprehension were global and did not depend on the side of shoulder instability. Accordingly, we could not observe differences between patients with left shoulder instability vs control participants or those patients with right shoulder instability vs control participants, nor in the direct comparison between patients with left vs right shoulder apprehension. Third, we investigated only shoulder apprehension. We assumed that the neuronal activations associated with apprehension and anxiety were not limited to shoulder apprehension, but a general effect of apprehension including, for example, knee instability; this remains to be elucidated in future studies. Finally, we assumed that apprehension is a dynamic process, which may change with time (eg, after shoulder stabilization). In this investigation, we only assessed the phase
16
of apprehension in patients with acute shoulder instability. Longitudinal assessment of patients with shoulder apprehension would be most interesting because the dynamic installation of apprehension-related changes in brain activations could be assessed, as well as modifications in brain activations with time associated with different treatment strategies in the sense of a surrogate marker.
CONCLUSIONS
Although shoulder apprehension is a well-known problem in sports medicine, the underlying mechanisms remain poorly understood. We demonstrate changes in neuronal processing associated with shoulder apprehension, indicating that apprehension is more complex than a pure mechanical problem of the shoulder. This also explains why mechanical stabilization alone oftentimes provides unsatisfactory results.
17 FIGURES AND LEGENDS
Figure 1 :
Fig 1: Visual rating ranging from unpleasant (−1) to pleasant (+1) associated with the presentation of APPREHENSION and CONTROL videos for patients and healthy volunteers. APPREHENSION vs CONTROL videos were associated with reduced rating values for both groups (P < .0001 corrected patients; P < .001 corrected
18 Figure 2 :
Fig 2: Patients vs control participants had a significantly (P < .05 corrected) higher task-correlated functional connectivity in 2 almost mirror symmetric components (IC 12 + 148% Smode in the right hemisphere; IC 17 + 144% Smode in left hemisphere).
These networks include the primary sensory-motor areas compatible with motor resistance, dlPFC associated with cognitive control of motor behavior and dACC/dmPFC associated with emotional regulation. In contrast, patients had significantly reduced functional connectivity in a bilateral higher-level visual and parietal network (IC 30 −185% Smode). Moreover, this component is anticorrelated with the video presentation, in contrast to the components IC 12 and IC 17. Axial sections of the spatial representation of the ICs 12, 17, and 30 are illustrated at the top. The inserts at the bottom represent the average time courses of these ICs (left) and the corresponding Fourier spectra (right).
19 Figure 3:
Fig 3: Correlation analysis between the Smodes (measure of activation strength) of task-positive (ICs 12 and 17) minus task-negative (IC 30) networks, and the participant average behavioral rating of unpleasantness. The negative correlation (rho = −0.4687) was significant (P = .022). Red “x” indicates individual patients; blue
“o” indicates individual healthy volunteer data.
20 Figure 4 :
Fig 4: Hypothesis-driven GLM analysis for contrast of APPREHENSION videos vs CONTROL videos. Patients vs healthy volunteers had increased activation in the left primary sensory-motor area and dlPFC overlapping with IC 17, yet at a lower degree of significance. The corresponding contralateral regions showed a clear trend, which remained just below multiple comparisons corrected threshold (not shown). The inverse comparison of healthy volunteers vs patients yielded no significant differences.
21 REFERENCES
1. Rowe CR, Zarins B. Recurrent transient subluxation of the shoulder. J Bone Joint Surg Am 1981;63:863–72
2. Jobe FW, Kvitne RS, Giangarra CE. Shoulder pain in the overhand or throwing athlete. The relationship of anterior instability and rotator cuff impingement. Orthop Rev 1989;18:963–75
3. Burkhart SS, De Beer JF. Traumatic glenohumeral bone defects and their relationship to failure of arthroscopic Bankart repairs: significance of the inverted-pear glenoid and the humeral engaging Hill-Sachs lesion. Arthroscopy 2000;16:677–94
4. Beckmann CF, Smith SM. Tensorial extensions of independent component analysis for multisubject fMRI analysis. Neuroimage 2005;25:294–311
5. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 1999;10:626–34
6. Beckmann CF, Smith SM. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 2004;23:137–52 7. Beckmann CF, Jenkinson M, Smith SM. General multilevel linear modeling for group
analysis in fMRI. Neuroimage 2003;20:1052–63
8. Woolrich MW, Behrens TE, Beckmann CF, et al. Multilevel linear modelling for fMRI group analysis using Bayesian inference. Neuroimage 2004;21:1732–47
9. Woolrich M. Robust group analysis using outlier inference. Neuroimage 2008;41:286–301
10. Smith SM, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics:
voxelwise analysis of multi-subject diffusion data. Neuroimage 2006;31:1487–505 11. Smith SM, Johansen-Berg H, Jenkinson M, et al. Acquisition and voxelwise analysis
of multi-subject diffusion data with tract-based spatial statistics. Nat Protoc 2007;2:499–503
12. Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 2009;44:83–98
13. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23 Suppl 1:S208–
19
14. Raichle ME. Two views of brain function. Trends Cogn Sci 2010;14:180–90
15. Büchel C, Coull JT, Friston KJ. The predictive value of changes in effective connectivity for human learning. Science 1999;283:1538–41
16. Bassett DS, Wymbs NF, Porter MA, et al. Dynamic reconfiguration of human brain
22
networks during learning. Proc Natl Acad Sci U S A 2011;108:7641–46
17. Lewis CM, Baldassarre A, Committeri G, et al. Learning sculpts the spontaneous activity of the resting human brain. Proc Natl Acad Sci U S A 2009;106:17558–63 18. Haller S, Kopel R, Jhooti P, et al. Dynamic reconfiguration of human brain functional
networks through neurofeedback. Neuroimage 2013;81C:243–52
19. Greicius MD, Krasnow B, Reiss AL, et al. Functional connectivity in the resting brain:
a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A 2003;100:253–58
20. Damoiseaux JS, Rombouts SA, Barkhof F, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 2006;103:13848–53
21. Cieslik EC, Zilles K, Caspers S, et al. Is there “one” DLPFC in cognitive action control? Evidence for heterogeneity from co-activation-based parcellation. Cereb Cortex 2012 Aug 23. [Epub ahead of print]
22. Hamilton JP, Etkin A, Furman DJ, et al. Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data. Am J Psychiatry 2012;169:693–703
23. Korgaonkar MS, Grieve SM, Etkin A, et al. Using standardized fMRI protocols to identify patterns of prefrontal circuit dysregulation that are common and specific to cognitive and emotional tasks in major depressive disorder: first wave results from the iSPOT-D study. Neuropsychopharmacology 2013;38:863–71
24. Mechias ML, Etkin A, Kalisch R. A meta-analysis of instructed fear studies:
implications for conscious appraisal of threat. Neuroimage 2010;49:1760–68
25. Straube T, Mentzel HJ, Miltner WH. Waiting for spiders: brain activation during anticipatory anxiety in spider phobics. Neuroimage 2007;37:1427–36
26. Holtz K, Pané-Farré CA, Wendt J, et al. Brain activation during anticipation of interoceptive threat. Neuroimage 2012;61:857–65
27. Schunck T, Erb G, Mathis A, et al. Test-retest reliability of a functional MRI anticipatory anxiety paradigm in healthy volunteers. J Magn Reson Imaging 2008;27:459–68
28. Simmons AN, Stein MB, Strigo IA, et al. Anxiety positive subjects show altered processing in the anterior insula during anticipation of negative stimuli. Hum Brain Mapp 2011;32:1836–46
29. Eryilmaz H, Van De Ville D, Schwartz S, et al. Impact of transient emotions on functional connectivity during subsequent resting state: a wavelet correlation approach. Neuroimage 2011;54:2481–91
23
30. Van De Ville D, Jhooti P, Haas T, et al. Recovery of the default mode network after demanding neurofeedback training occurs in spatio-temporally segregated subnetworks. Neuroimage 2012;63:1775–81
24
Article 2: Neural correlates of clinical scores in patients with shoulder apprehension
INTRODUCTION
Traumatic anterior glenohumeral dislocation is the most frequent type of joint instability and affects approximately 1.7% of the general population.(36) The majority of patients have favorable outcomes after open or arthroscopic stabilization.(9,17,21) However, complications such as recurrent shoulder instability or persistent apprehension have been reported to range from 2% to 13%.(9,21,23,45) This can lead to increased morbidity for the patient: increased pain, decreased activity level, prolonged time away from work and sports, and a general decrease in life quality.
Apprehension is a common sign of anterior glenohumeral instability defined by fear of imminent dislocation elicited when bringing the arm to 90° of abduction and 90°
of external rotation. This test has been found to be a particularly accurate predictor for shoulder instability.(8,11,26,30) Functional magnetic resonance imaging (fMRI) was recently used to explore the neuronal mechanisms underlying apprehension and found a complex cerebral reorganization in patients with shoulder instability, mainly in the primary sensitive and motor cortex, and in the anxiety networks.(15) This could explain why some patients still complain about persistent apprehension in the absence of any proven recurrence of instability.(4,17)
The current investigation extends these previous findings to further disentangle the cognitively complex mechanism of shoulder apprehension, which includes several high-level processes such as anxiety, salience, fear and anticipation. In particular, we correlated five clinically established scores and tests that assess these different aspects of apprehension to brain activation patterns from functional MRI in patients with a positive apprehension sign.
25 MATERIAL AND METHODS
Patient Selection
Between 2011 and 2014, all patients with shoulder instability evaluated in a shoulder clinic were considered potentially eligible for this prospective study. Inclusion criteria included right-handed male patients with a positive shoulder apprehension test.
Exclusion criteria were a history of drug or alcohol abuse, major medical disorders or use of medication such as psychotropics, stimulants or β-blockers. Institutional ethics committee approval was obtained before the study began and the subjects signed a written informed consent form prior to participation.
This study included a cohort of 28 patients (18 with right-sided and 10 with left-sided glenohumeral instability) with a mean age of 26.8 ± 1.2 years (range, 17 to 46 years).
Ten healthy, male, right-handed and age-matched (29.6 ± 1.3 years) participants were selected from the general population. The control volunteers had no history of shoulder injury, instability or hyperlaxity. The latter was defined as more than 85° of external rotation elbow against waist,(10) or hyper abduction over 105°.(12)
Clinical scores assessment
All patients were assessed with five commonly used subjective scores in the form of self-administered questionnaires (Table 1), prior to fMRI. The pain Visual Analog Scale (VAS)(18) is a widely used single item score where the patient rates pain intensity between zero and ten. This scale is useful for patient pre- and postoperative monitoring, and has also been correlated to patient anxiety.(29) TheSimple Shoulder Test (SST)(25) consists of twelve binary “yes” or “no” questions evaluating shoulder performance in daily activities. This is a general shoulder questionnaire which is used in a broad range of shoulder conditions. Subjective Shoulder Value (SSV) is a single question, where the patient is asked to rate his overall shoulder function as a percentage of a normal shoulder.(14) It is a quick and easily administered score that has also been validated for various shoulder disabilities, such as instability. The Rowe score for instability(37) is a 3 item score with 4 choices each, measuring shoulder function, stability and motion. The final result is converted to a value
26
between 0 and 100. This score has been specifically developed for shoulder instability. Finally, the Western Ontario Shoulder Instability (WOSI)(22) score is a 21 items score also specific for shoulder instability, measuring the degree of disability in activities of daily living. The final result is also converted to a value between 0 and 100. Except for pain VAS, higher results mean higher function.
fMRI Acquisition
Images were obtained using a 3T scanner (Trio; Siemens, Erlangen, Germany) with a standard 32-channel head-coil. fMRI imaging of the whole brain was performed using echo planar imaging employing the following parameters: whole brain coverage, 96x96 matrix, TR=2.5s, TE=30ms, 39 slices, 148 repetitions. A 3D T1- weighted structural scan (256x256 matrix size, 176 sections, 1x1x1 mm3, TE=2.3ms, TR=2300ms) and a diffusion tensor imaging (DTI) scan (30 diffusion directions b=1000 s/mm2 isotropically distributed on a sphere, 1 reference b=0 s/mm2 image with no diffusion weighting, 128x64 matrix, 2x2x2mm voxel size, TE=92ms,TR=9000ms, 1 average) were acquired.
fMRI task
The paradigm consisted of an on-off block-design with two active conditions (apprehension cue and control videos) and a resting condition (Figure 1). During the active condition video cues were used(15) these animation movies (10 seconds) showed common activities that trigger shoulder apprehension. Control videos were matched for content except for the absence of cues inducing shoulder apprehension.
After each video, a visual analog scale was presented for 2.5 seconds and participants rated the degree of perceived apprehension using an MR-compatible response box. The rating scale included nine steps from no apprehension to high apprehension. After the rating, a rest period followed, including the visual presentation of a fixation cross for 17.5 seconds. Each participant performed two runs. Within each run, lasting for 370 seconds each, 6 apprehension and 6 control videos were shown in a pseudo-randomized fashion. Before MRI scanning,
27
participants were familiarized with the procedure and performed a training run outside the scanner.
Statistical Analysis
Statistical analyses were conducted using GraphPad Prism (Version 6, GraphPad Software, San Diego, USA) and FSL (Version 5.0.6, FMRIB, Oxford, UK).
Analysis of Clinical and Demographic data
After performing D’Agostino-Pearson omnibus test to check for normal distribution, those variables that were normally distributed, notably pain VAS, Rowe, SSV, WOSI and SST scores, were submitted to Pearson correlations. The participants’ age, non- normally distributed, was analyzed using a Mann-Whitney test.
Functional Connectivity Analyses and Correlation with Clinical scores
Independent component analysis (ICA) was carried out using FSL’s multi-session multivariate exploratory linear optimized decomposition into independent components (MELODIC) tensor ICA(2) setting the number of components to 25 which is common practice in ICA for fMRI data. The data structure is arranged as subjects x space x time for the tensor decomposition; i.e., each independent component (IC) comes with an s-mode vector (measure of strength of this IC for the subjects), a spatial map, and a time-course. To test correlations between the connectivity of different brain areas and the clinical test scores, Pearson correlation analyses were performed between the s-mode values (a measure of the activation strength) and the final scores of each test with False Discovery Rate (FDR) correction for multiple comparisons.(3) To test for differences in activation of different brain networks between patients and controls, the s-mode values were compared between groups using a 2-sample t-test with FDR correction for multiple comparisons.
28
Post-hoc GLM activation correlation with clinical scores
Processing and analysis of imaging data was performed using FSL FEAT (fMRI Expert Analysis Tool version 6.00, http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT).
Preprocessing included brain extraction using FSL’s BET (Brain Extraction Tool), motion correction using FSL’s MCFLIRT (intra-modal motion correction tool) (20) and smoothing using FSL’s SUSAN (noise reduction uses nonlinear filtering.(39) A general linear model (GLM) was employed at three levels of analyses. At the first level, the contrast of apprehension versus control videos was calculated individually for each run of each participant using a fixed-effects analysis. Then, at the second level, a fixed-effects analysis combined both runs of each participant. Finally, at the third level, the 2nd level imaging results were correlated to the scores of the clinical tests for each participant (pain VAS, Rowe, SST, SSV and WOSI). The main predictor was the demeaned and normalized (values between -1 and 1) behavioral score for each subject. Finally, a correction for multiple comparisons by threshold- free cluster enhancement TFCE (47)was applied. P values < 0.05 were considered as significant.
VBM Analysis of T1 Images
To assess gray matter density differences between groups, a voxel-based morphometry (VBM) analysis was performed in FSL (FSL Version 5.0.6;
http://fsl.fmrib.ox.ac.uk) using standard processing steps.40,42 First, BET extraction and tissue-type segmentation were performed using the corresponding FSL tools (Brain Extraction Tool and FAST4). Next, a non-linear transformation into Montreal Neurological Institute (MNI) reference space was applied and a study-specific gray matter (GM) template was created. The native GM images were then non-linearly registered to this template. Finally, the images were smoothed with an isotropic Gaussian kernel of 2 mm sigma at width-at-half-maximum (FWHM). A voxel-wise GLM was implemented using permutation-based nonparametric testing (Randomize, part of FSL). Results were corrected for multiple comparisons using TFCE (43) and P values <0.05 were considered as significant.
29 TBSS Analysis of DTI Data
FSL (FSL Version 5.0.6; http://fsl.fmrib.ox.ac.uk) software was used to analyze diffusion tensor imaging (DTI) data, according to the standard procedure (41) to test for differences of white matter integrity between groups. First, all subjects’ fractional anisotropy data was projected onto a mean fractional anisotropy tract skeleton by non-linear registration. Later, by using a non-linear registration voxel-wise statistical analysis with threshold free cluster enhancement correction for multiple comparisons was performed, considering TFCE corrected P values <0.05 as significant.
RESULTS Clinical scores
Mean score results were 4.1 ± 2.47 for pain VAS, 8.97 ±2.06 for SST, 62,52 ±50.77 for SSV, 36.90 ±19.43 for Rowe and 50.77 ±21.45 for WOSI (Table 1). Significant correlations (p<0.05, multiple comparisons corrected) were found between the test scores of all the clinical tests except between Rowe – SSV, and Rowe - SST (Table 2).
Functional Connectivity Analyses and Correlation with Clinical scores
Significant results (p<0.05, FDR multiple comparison corrected) were found for the correlation analyses between the s-mode values of different ICs and the final scores of pain VAS, Rowe and WOSI scores. Specifically, final scores of pain VAS, Rowe and WOSI tests positively correlate with the connectivity strength of four almost overlapping networks (IC1, IC2, IC3, IC4) notably including the bilateral anterior insula (aINS), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), bilateral dorsomedial prefrontal cortex (dmPFC), supplementary motor area (SMA) and somatosensory cortex (Figure 2). Additionally, Rowe correlated with the strengths of the components IC5, IC6, IC7, involving networks overlapping to the previous ones plus in addition anterior mid-cingulate cortex and visual and attentional areas (Figure 2). Finally, group analyses showed significant differences (p<0.01) between patients and controls in brain networks, qualitatively replicating the results of
30
a previous study.(15) The s-mode values of this component significantly correlate (p<0.05) with the final scores of pain VAS and Rowe scores.
Post-hoc GLM activation correlation with clinical scores
From the post-hoc correlation analyses between GLM activations and clinical scores, two of the five clinical tests yielded significant correlations. The Rowe score correlated with activity in bilateral frontal pole and in the posterior division of the left inferior temporal gyrus (Figure 3a and Table 3). The SSV test correlated with activity in bilateral pre- / post-central gyrus and bilateral superior parietal lobe (Figure 3b and Table 3).
VBM and TBSS Analysis
The VBM analysis of gray matter (GM) density and the TBSS analysis of white matter (WM) revealed no statistical differences between study groups.
DISCUSSION
Shoulder apprehension is a cognitively complex condition involving many aspects such as anxiety, anticipation, salience and fear. In a previous study, global changes in cerebral networks were demonstrated for the first time in shoulder apprehension by the comparison of patients versus controls.(15) The current investigation extends these observations and further unravels the complex condition of apprehension by correlating five established clinical scores and tests to functional imaging in patients with a positive apprehension sign undergoing visual cue apprehension stimulation.
These five clinical scores assess different aspects of shoulder apprehension.
Consequently, the corresponding brain activations partially overlap due to common general aspects of apprehension, such as anxiety and pain regulation, notably for Rowe, pain VAS and WOSI. Conversely, the test-related brain networks partially diverge, notably between Rowe and SSV. This partial divergence in brain activation
31
associated with different clinical scores is in agreement with the fact that they assess different aspects of the complex condition of shoulder apprehension.
In a first step, we analyzed the correlation between the five different clinical scores at the behavioral level to evaluate how much they diverge from each other and how much they capture the same components of shoulder apprehension. Significant correlations were found between all the different tests except between Rowe - SST and Rowe - SSV. These results prove that the tests are able to measure a common aspect of shoulder apprehension, but as shown by the limited shared variance (between 30% and 60%) the overlapping is partial and the tests diverge qualitatively from each other. Specifically, considering only our behavioral results, pain VAS and WOSI are the scores that share the largest amount of variance with the other tests.
Instead, SST, SSV and Rowe measure significantly different phenomena between each other.
In a second step, we correlated functional connectivity networks to these 5 different clinical scores to disentangle overlapping and divergent neuronal networks related to these different tests. Pain VAS, Rowe, and WOSI were correlated with partially overlapping functional networks in the brain (i.e., components IC1, IC2, IC3, IC4) involving notably activations of dorsolateral/dorsomedial pre-frontal cortex, dorsal anterior cingulate cortex (dACC) and somatosensory areas, as well as deactivations of ventral anterior and posterior cingulate cortex (vACC, PCC) and precuneus (Figure 2). This network resembles the default mode network (DMN) that is a resting-state network involved in many clinical conditions and, in particular, in negative mood states,(7) anxiety,(32)and in pain regulation during painful situations.(1) Specifically, medial and dorsolateral prefrontal areas are involved in pain modulation,(6,27) expectancy of pain (16,38) and interaction between pain and anxiety,(35) while the connectivity between ACC and PCC is involved in pain stimulation processing.(13) This indicates that pain VAS, Rowe and WOSI are scores that measure pain expectancy and pain-related movement induced by shoulder apprehension. A higher score means higher activity of these areas to activate these motor and cognitive processes. Rowe additionally presented significant correlations with three partially overlapping components (IC5, IC6, IC7) showing additional recruitment of the frontal pole and of occipital brain areas. The frontal pole activity during pain-related stimulations is generally related to cognitive and attentional processing,(5) while
