Search

Background Sleep disturbances are common in Parkinson’s Disease (PD), with nocturnal akinesia being one of the most burdensome. Levodopa is frequently used in clinical routine to improve nocturnal akinesia, although evidence is not well proven. Methods We assessed associations of Levodopa intake with quality of sleep and perception of nocturnal akinesia in three PD cohorts, using the Parkinson’s Disease Sleep Scale (PDSS-2) in two cohorts and a question on nocturnal immobility in one cohort. In one cohort also objective assessment of mobility during sleep was performed, using mobile health technology. Results In an independent analysis of all three cohorts (in total n = 1124 PD patients), patients taking Levodopa CR reported a significantly higher burden by nocturnal akinesia than patients without Levodopa. Higher Levodopa intake and MDS-UPDRS part IV scores (indicating motor fluctuations) predicted worse PDSS-2 and higher subjective nocturnal immobility scores, while disease duration and severity were not predictive. Levodopa intake was not associated with objectively changed mobility during sleep. Conclusion Our results showed an association of higher Levodopa intake with perception of worse quality of sleep and nocturnal immobility in PD, indicating that Levodopa alone might not be suitable to improve subjective feeling of nocturnal akinesia in PD. In contrast, Levodopa intake was not relevantly associated with objectively measured mobility during sleep. PD patients with motor fluctuations may be particularly affected by subjective perception of nocturnal mobility. This study should motivate further pathophysiological and clinical investigations on the cause of perception of immobility during sleep in PD.

Geriatric medicine is a rapidly evolving field that addresses diagnostic, therapeutic and care aspects of older adults. Some disabilities and disorders affecting cognition (e.g. dementia), motor function (e.g. stroke, Parkinson’s disease, neuropathies), mood (e.g. depression), behavior (e.g. delirium) and chronic pain disorders are particularly frequent in old subjects. As knowledge about these age-associated conditions and disabilities is steadily increasing, the integral implementation of neurogeriatric knowledge in geriatric medicine and specific neurogeriatric research is essential to develop the field. This article discusses how neurological know-how could be integrated in academic geriatric medicine to improve care of neurogeriatric patients, to foster neurogeriatric research and training concepts and to provide innovative care concepts for geriatric patients with predominant neurological conditions and disabilities.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing. publishedVersion

ZusammenfassungPatienten mit idiopathischem Parkinson-Syndrom (IPS) erkranken deutlich häufiger als die Allgemeinheit an Osteoporose. Dies kann sowohl auf Gemeinsamkeiten in der Pathogenese als auch auf die Begleiterkrankungen des IPS, von denen zahlreiche Risikofaktoren für Osteoporose darstellen, zurückgeführt werden. In diesem Artikel werden verschiedene Risikofaktoren vorgestellt, auf die in der Praxis geachtet werden sollte. Insbesondere wird auf Vitamin D-Mangel, Hyperhomocysteinämie in Zusammenhang mit Levodopa-Einnahme und Mangelernährung sowie Konsequenzen der Immobilität eingegangen. Aufgrund des erhöhten Osteoporoserisikos ist es sinnvoll, dass nach der Diagnosestellung des IPS eine Basisdiagnostik der Osteoporose erfolgt und die IPS-Patienten auch im Verlauf immer wieder auf Osteoporose untersucht werden. Obwohl der Zusammenhang von IPS und Osteoporose allgemein bekannt ist, führt die Diagnose einer Osteoporose beim IPS häufig nicht zu einer ausreichenden Therapie. Konkret kann die Osteoporose bei IPS-Patienten durch Physio- und Ergotherapie, eine Anpassung von Lebensstil (Reduzierung von Alkohol und Nikotinkonsum) und Ernährung, großzügige Supplementierung von Vitamin D und durch Gabe einer spezifischen antiosteoporotischen Medikation, wie z. B. Bisphosphonaten, behandelt werden. Eine Studie, die noch validiert werden muss, zeigt auch einen positiven Effekt einer Substitution von Folsäure und Cobalamin, um die Homocystein-Serumkonzentration zu senken. Bei der medikamentösen Behandlung ist bei IPS-Patienten auch insbesondere zu beachten, dass die orale Einnahme der Medikamente einen suffizienten Schluckakt voraussetzt. Für IPS-Patienten mit kritischer Dysphagie stehen auch parenterale Applikationsformen zur Verfügung. Aufgrund der Komplexität der Behandlung des IPS und der Osteoporose bedarf es somit eines interdisziplinären und multiprofessionellen Behandlungsteams, das Neurologen, Geriater und Ernährungsmediziner mit einschließt.

Static balance is a commonly used health measure in clinical practice. Usually, static balance parameters are assessed via force plates or, more recently, with inertial measurement units (IMUs). Multiple parameters have been developed over the years to compare patient groups and understand changes over time. However, the day-to-day variability of these parameters using IMUs has not yet been tested in a neurogeriatric cohort. The aim of the study was to examine day-to-day variability of static balance parameters of five experimental conditions in a cohort of neurogeriatric patients using data extracted from a lower back-worn IMU. A group of 41 neurogeriatric participants (age: 78 ± 5 years) underwent static balance assessment on two occasions 12–24 h apart. Participants performed a side-by-side stance, a semi-tandem stance, a tandem stance on hard ground with eyes open, and a semi-tandem assessment on a soft surface with eyes open and closed for 30 s each. The intra-class correlation coefficient (two-way random, average of the k raters’ measurements, ICC2, k) and minimal detectable change at a 95% confidence level (MDC95%) were calculated for the sway area, velocity, acceleration, jerk, and frequency. Velocity, acceleration, and jerk were calculated in both anterior-posterior (AP) and medio-lateral (ML) directions. Nine to 41 participants could successfully perform the respective balance tasks. Considering all conditions, acceleration-related parameters in the AP and ML directions gave the highest ICC results. The MDC95% values for all parameters ranged from 39% to 220%, with frequency being the most consistent with values of 39–57%, followed by acceleration in the ML (43–55%) and AP direction (54–77%). The present results show moderate to poor ICC and MDC values for IMU-based static balance assessment in neurogeriatric patients. This suggests a limited reliability of these tasks and parameters, which should induce a careful selection of potential clinically relevant parameters.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments.With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

IntroductionAdvances in wearable sensor technology now enable frequent, objective monitoring of real-world walking. Walking-related digital mobility outcomes (DMOs), such as real-world walking speed, have the potential to be more sensitive to mobility changes than traditional clinical assessments. However, it is not yet clear which DMOs are most suitable for formal validation. In this review, we will explore the evidence on discriminant ability, construct validity, prognostic value and responsiveness of walking-related DMOs in four disease areas: Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and proximal femoral fracture. Methods and analysisArksey and O’Malley’s methodological framework for scoping reviews will guide study conduct. We will search seven databases (Medline, CINAHL, Scopus, Web of Science, EMBASE, IEEE Digital Library and Cochrane Library) and grey literature for studies which (1) measure differences in DMOs between healthy and pathological walking, (2) assess relationships between DMOs and traditional clinical measures, (3) assess the prognostic value of DMOs and (4) use DMOs as endpoints in interventional clinical trials. Two reviewers will screen each abstract and full-text manuscript according to predefined eligibility criteria. We will then chart extracted data, map the literature, perform a narrative synthesis and identify gaps. Ethics and disseminationAs this review is limited to publicly available materials, it does not require ethical approval. This work is part of Mobilise-D, an Innovative Medicines Initiative Joint Undertaking which aims to deliver, validate and obtain regulatory approval for DMOs. Results will be shared with the scientific community and general public in cooperation with the Mobilise-D communication team. RegistrationStudy materials and updates will be made available through the Center for Open Science’s OSFRegistry (https://osf.io/k7395). This work was supported by the Mobilise-D project, that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs (i.e., self‐regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain‐based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning., Many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success, but the factors that cause this vast variability between participants remain unknown. Here, we used a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs could predict neurofeedback learning success. We were not able to identify common brain‐based success predictors across our diverse cohort of studies.

Reference EPFL-ARTICLE-201360doi:10.1007/s00234-014-1360-0View record in Web of Science Record created on 2014-08-29, modified on 2017-05-10

Although many smokers try to quit smoking, only about 20-25 percent will achieve abstinence despite 6 months or more of gold-standard treatment. This low success rate suggests long-term changes in the brain related to smoking, which remain poorly understood. We compared ex-smokers to both active smokers and non-smokers using functional magnetic resonance imaging (fMRI) to explore persistent modifications in brain activity and network organization. This prospective and consecutive study includes 18 non-smokers (29.5 ± 6.7 years of age, 11 women), 14 smokers (≥10 cigarettes a day >2 years of smoking, 29.3 ± 6.0 years of age, 10 women) and 14 ex-smokers (>1 year of quitting 30.5 ± 5.7 years of age, 10 women). Participants underwent a block-design fMRI study contrasting smoking cue with control (neutral cue) videos. Data analyses included task-related general linear model, seed-based functional connectivity, voxel-based morphometry (VBM) of gray matter and tract-based spatial statistics (TBSS) of white matter. Smoking cue videos versus control videos activated the right anterior insula in ex-smokers compared with smokers, an effect correlating with cumulative nicotine intake (pack-years). Moreover, ex-smokers had a persistent decrease in functional connectivity between right anterior insula and anterior cingulate cortex (ACC) compared with control participants, but similar to active smokers. Potentially confounding alterations in gray or white matter were excluded in VBM and TBSS analyses. In summary, ex-smokers with long-term nicotine abstinence have persistent and dose-dependent brain network changes notably in the right anterior insula and its connection to the ACC.

An increasing number of studies using real-time fMRI neurofeedback have demonstrated that successful regulation of neural activity is possible in various brain regions. Since these studies focused on the regulated region(s), little is known about the target-independent mechanisms associated with neurofeedback-guided control of brain activation, i.e. the regulating network. While the specificity of the activation during self-regulation is an important factor, no study has effectively determined the network involved in self-regulation in general. In an effort to detect regions that are responsible for the act of brain regulation, we performed a post-hoc analysis of data involving different target regions based on studies from different research groups. We included twelve suitable studies that examined nine different target regions amounting to a total of 175 subjects and 899 neurofeedback runs. Data analysis included a standard first-(single subject, extracting main paradigm) and second-level (single subject, all runs) general linear model (GLM) analysis of all participants taking into account the individual timing. Subsequently, at the third level, a random effects model GLM included all subjects of all studies, resulting in an overall mixed effects model. Since four of the twelve studies had a reduced field of view (FoV), we repeated the same analysis in a subsample of eight studies that had a well-overlapping FoV to obtain a more global picture of self-regulation. The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum, were consistently active during the regulation of brain activation across the studies. The anterior insula has been implicated in interoceptive awareness of the body and cognitive control. Basal ganglia are involved in procedural learning, visuomotor integration and other higher cognitive processes including motivation. The larger FoV analysis yielded additional activations in the anterior cingulate cortex, the dorsolateral and ventrolateral prefrontal cortex, the temporo-parietal area and the visual association areas including the temporo-occipital junction. In conclusion, we demonstrate that several key regions, such as the anterior insula and the basal ganglia, are consistently activated during self-regulation in real-time fMRI neurofeedback independent of the targeted region-ofinterest. Our results imply that if the real-time fMRI neurofeedback studies target regions of this regulation network, such as the anterior insula, care should be given whether activation changes are related to successful regulation, or related to the regulation process per se. Furthermore, future research is needed to determine how activation within this regulation network is related to neurofeedback success. (C) 2015 Elsevier Inc. All rights reserved.

The emerging technique of real-time fMRI neurofeedback trains individuals to regulate their own brain activity via feedback from an fMRI measure of neural activity. Optimum feedback presentation has yet to be determined, particularly when working with clinical populations. To this end, we compared continuous against intermittent feedback in subjects with tinnitus. Fourteen participants with tinnitus completed the whole experiment consisting of nine runs (3 runs × 3 days). Prior to the neurofeedback, the target region was localized within the auditory cortex using auditory stimulation (1 kHz tone pulsating at 6 Hz) in an ON-OFF block design. During neurofeedback runs, participants received either continuous (n = 7, age 46.84 ± 12.01, Tinnitus Functional Index (TFI) 49.43 ± 15.70) or intermittent feedback (only after the regulation block) (n = 7, age 47.42 ± 12.39, TFI 49.82 ± 20.28). Participants were asked to decrease auditory cortex activity that was presented to them by a moving bar. In the first and the last session, participants also underwent arterial spin labeling (ASL) and resting-state fMRI imaging. We assessed tinnitus severity using the TFI questionnaire before all sessions, directly after all sessions and six weeks after all sessions. We then compared neuroimaging results from neurofeedback using a general linear model (GLM) and region-of-interest analysis as well as behavior measures employing a repeated-measures ANOVA. In addition, we looked at the seed-based connectivity of the auditory cortex using resting-state data and the cerebral blood flow using ASL data. GLM group analysis revealed that a considerable part of the target region within the auditory cortex was significantly deactivated during neurofeedback. When comparing continuous and intermittent feedback groups, the continuous group showed a stronger deactivation of parts of the target region, specifically the secondary auditory cortex. This result was confirmed in the region-of-interest analysis that showed a significant down-regulation effect for the continuous but not the intermittent group. Additionally, continuous feedback led to a slightly stronger effect over time while intermittent feedback showed best results in the first session. Behaviorally, there was no significant effect on the total TFI score, though on a descriptive level TFI scores tended to decrease after all sessions and in the six weeks follow up in the continuous group. Seed-based connectivity with a fixed-effects analysis revealed that functional connectivity increased over sessions in the posterior cingulate cortex, premotor area and part of the insula when looking at all patients while cerebral blood flow did not change significantly over time. Overall, these results show that continuous feedback is suitable for long-term neurofeedback experiments while intermittent feedback presentation promises good results for single session experiments when using the auditory cortex as a target region. In particular, the down-regulation effect is more pronounced in the secondary auditory cortex, which might be more susceptible to voluntary modulation in comparison to a primary sensory region., Highlights • Comparison of continuous and intermittent fMRI neurofeedback in tinnitus patients • Both groups attempted down-regulation of the auditory cortex. • The continuous feedback group seemed to improve after multiple sessions. • Intermittent feedback worked best for a single session. • Down-regulation effect seems more pronounced in the secondary auditory cortex.

Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) is an exciting neuroimaging application. In most rt-fMRI NF studies, the activity level of a single region of interest (ROI) is provided as a feedback signal and the participants are trained to up or down regulate the feedback signal. NF training effects are typically analyzed using a confirmatory univariate approach, i.e., changes in the target ROI are explained by a univariate linear modulation. However, learning to self-regulate the ROI activity through NF is mediated by distributed changes across the brain. Here, we deploy a multivariate decoding model for assessing NF training effects across the whole brain. Specifically, we first explain the NF training effect by a posthoc multivariate model that leads to a pattern of coactivation based on 90 functional atlas regions. We then use cross validation to reveal the set of brain regions with the best fit. This novel approach was applied to the data from a rt-fMRI NF study where the participants learned to down regulate the auditory cortex. We found that the optimal model consisted of 16 brain regions whose coactivation patterns best described the training effect over the NF training days. Cross validation of the multivariate model showed that it generalized across the participants. Interestingly, the participants could be clustered into two groups with distinct patterns of coactivation, potentially reflecting different NF learning strategies. Overall, our findings revealed that multiple brain regions are involved in learning to regulate an activity in a single ROI, and thus leading to a better understanding of the mechanisms underlying NF training.

Introduction The fetal origin of the posterior cerebral artery (fPCA) is a frequent vascular variant in 11-29% of the population. For the fPCA, blood flow in the PCA originates from the anterior instead of the posterior circulation. We tested whether this blood supply variant impacts the cerebral blood flow assessed by arterial spin labeling (ASL), cerebrovascular reserve as well as resting-state static functional connectivity (sFC) in the sense of a systematic confound. Methods The study included 385 healthy, elderly subjects (mean age: 74.18 years [range: 68.9-90.4]; 243 female). Participants were classified into normal vascular supply (n = 296, 76.88%), right fetal origin (n = 23, 5.97%), left fetal origin (n = 16, 4.16%), bilateral fetal origin (n = 4, 1.04%), and intermediate (n = 46, 11.95%, excluded from further analysis) groups. ASL-derived relative cerebral blood flow (relCBF) maps and cerebrovascular reserve (CVR) maps derived from a CO2 challenge with blocks of 7% CO2 were compared. Additionally, sFC between 90 regions of interest (ROIs) was compared between the groups. Results CVR was significantly reduced in subjects with ipsilateral fPCA, most prominently in the temporal lobe. ASL yielded a non-significant trend towards reduced relCBF in bilateral posterior watershed areas. In contrast, conventional atlas-based sFC did not differ between groups. Conclusions In conclusion, fPCA presence may bias the assessment of cerebrovascular reserve by reducing the response to CO2. In contrast, its effect on ASL-assessed baseline perfusion was marginal. Moreover, fPCA presence did not systematically impact resting-state sFC. Taken together, this data implies that perfusion variables should take into account the vascularization patterns.

Although many smokers try to quit smoking, only about 20-25 percent will achieve abstinence despite 6 months or more of gold-standard treatment. This low success rate suggests long-term changes in the brain related to smoking, which remain poorly understood. We compared ex-smokers to both active smokers and non-smokers using functional magnetic resonance imaging (fMRI) to explore persistent modifications in brain activity and network organization. This prospective and consecutive study includes 18 non-smokers (29.5 ± 6.7 years of age, 11 women), 14 smokers (≥10 cigarettes a day2 years of smoking, 29.3 ± 6.0 years of age, 10 women) and 14 ex-smokers (1 year of quitting 30.5 ± 5.7 years of age, 10 women). Participants underwent a block-design fMRI study contrasting smoking cue with control (neutral cue) videos. Data analyses included task-related general linear model, seed-based functional connectivity, voxel-based morphometry (VBM) of gray matter and tract-based spatial statistics (TBSS) of white matter. Smoking cue videos versus control videos activated the right anterior insula in ex-smokers compared with smokers, an effect correlating with cumulative nicotine intake (pack-years). Moreover, ex-smokers had a persistent decrease in functional connectivity between right anterior insula and anterior cingulate cortex (ACC) compared with control participants, but similar to active smokers. Potentially confounding alterations in gray or white matter were excluded in VBM and TBSS analyses. In summary, ex-smokers with long-term nicotine abstinence have persistent and dose-dependent brain network changes notably in the right anterior insula and its connection to the ACC.

Introduction Anterior shoulder apprehension is a commonly reported complaint in anterior shoulder instability, which may lead to patient morbidity and impede shoulder function. It is the result of a cognitively complex mechanism, which includes anxiety, salience, fear, and anticipation. Purpose The aim of this prospective case-control study was to correlate five clinically established scores using functional magnetic resonance imaging to assess brain activation patterns in patients with apprehension related to anterior shoulder instability. Methods This study includes 28 consecutive male right-handed patients ( mean +/- SEM, 26.8 +/- 1.2 yr) with positive shoulder apprehension test and 10 healthy matched control participants without apprehension or a history of instability. Task- related and functional connectivity functional magnetic resonance imaging activation patterns occurring during apprehension video cue stimulation were correlated with five clinical tests and scores: Visual Analog Scale ( VAS), Rowe score for instability, Simple Shoulder Test, Subjective Shoulder Value ( SSV), and Western Ontario Shoulder Instability ( WOSI). Results Rowe, pain VAS, and WOSI scores correlated with prefrontal cortex, dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, somatosensory area, and parieto-occipital and temporal areas (default mode network). Rowe score additionally correlated with frontal pole, anterior midcingulate cortex, and visual areas. Moreover, SSV correlated with task-related brain activity in the bilateral precentral gyrus, bilateral postcentral gyrus, and bilateral superior parietal lobe. Conclusions Overall, Rowe score provides the strongest link between shoulder apprehension and brain level alterations as it correlates with the highest number of independent components involving areas responsible for both motor and cognitive functions, whereas pain VAS and WOSI occupy an intermediately strong link recruiting less brain networks. Finally, Simple Shoulder Test and SSV have the weakest link at the brain level.

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is used as a tool to gain voluntary control of activity in various brain regions. Little emphasis has been put on the influence of cognitive and personality traits on neurofeedback efficacy and baseline activity. Here, we assessed the effect of individual pain coping on rt-fMRI neurofeedback during heat-induced pain. Twenty-eight healthy subjects completed the Coping Strategies Questionnaire (CSQ) prior to scanning. The first part of the fMRI experiment identified target regions using painful heat stimulation. Then, subjects were asked to down-regulate the pain target brain region during four neurofeedback runs with painful heat stimulation. Functional MRI analysis included correlation analysis between fMRI activation and pain ratings as well as CSQ ratings. At the behavioral level, the active pain coping (first principal component of CSQ) was correlated with pain ratings during neurofeedback. Concerning neuroimaging, pain sensitive regions were negatively correlated with pain coping. During neurofeedback, the pain coping was positively correlated with activation in the anterior cingulate cortex, prefrontal cortex, hippocampus and visual cortex. Thermode temperature was negatively correlated with anterior insula and dorsolateral prefrontal cortex activation. In conclusion, self-reported pain coping mechanisms and pain sensitivity are a source of variance during rt-fMRI neurofeedback possibly explaining variations in regulation success. In particular, active coping seems to be associated with successful pain regulation. Electronic supplementary material The online version of this article (doi:10.1007/s11682-016-9547-0) contains supplementary material, which is available to authorized users.

© 2014 Emmert Breimhorst Bauermann Birklein Van De Ville and Haller. Real time functional magnetic resonance imaging (rt fMRI) neurofeedback allows learning voluntary control over specific brain areas by means of operant conditioning and has been shown to decrease pain perception. To further increase the effect of rt fMRI neurofeedback on pain we directly compared two different target regions of the pain network notably the anterior insular cortex (AIC) and the anterior cingulate cortex (ACC). Participants for this prospective study were randomly assigned to two age matched groups of 14 participants each (7 females per group) for AIC and ACC feedback. First a functional localizer using block design heat pain stimulation was performed to define the pain sensitive target region within the AIC or ACC. Second subjects were asked to down regulate the BOLD activation in four neurofeedback runs during identical pain stimulation. Data analysis included task related and functional connectivity analysis. At the behavioral level pain ratings significantly decreased during feedback vs. localizer runs but there was no difference between AIC and ACC groups. Concerning neuroimaging ACC and AIC showed consistent involvement of the caudate nucleus for subjects that learned down regulation (17/28) in both task related and functional connectivity analysis. The functional connectivity toward the caudate nucleus is stronger for the ACC while the AIC is more heavily connected to the ventrolateral prefrontal cortex. Consequently the ACC and AIC are suitable targets for real time fMRI neurofeedback during pain perception as they both affect the caudate nucleus although functional connectivity indicates that the direct connection seems to be stronger with the ACC. Additionally the caudate an important area involved in pain perception and suppression could be a good rt fMRI target itself. Future studies are needed to identify parameters characterizing successful regulators and to assess the effect of repeated rt fMRI neurofeedback on pain perception.