Understanding Fear after an Anterior Cruciate Ligament Injury: A Qualitative Thematic Analysis Using the Common-Sense Model.

Fear is a significant factor affecting successful return to sport following an anterior cruciate ligament (ACL) injury. However, there is a lack of understanding of the emotional drivers of fear and how fear beliefs are formed. This study qualitatively explored the contextual and emotional underpinnings of fear and how these beliefs were formed, with reference to the Common-Sense Model of Self-Regulation. Face-to-face online interviews were conducted with ACL-injured participants (n = 18, 72% female) with a mean age of 28 years (range 18-50 years). Participants were either 1 year post ACL reconstruction surgery (n = 16) or at least 1 year post injury without surgery (n = 2) and scored above average on a modified Tampa Scale of Kinesiophobia. Four participants were playing state-level sport or higher. Five themes emerged describing factors contributing to fear: 'External messages', 'Difficulty of the ACL rehabilitation journey', 'Threat to identity and independence', 'Socioeconomic factors', and 'Ongoing psychological barriers'. A sixth theme, 'Positive coping strategies', provided insight into influences that could reduce fear and resolve negative behaviors. This study identified a broad range of contextual biopsychosocial factors which contribute to fear, supporting the notion that ACL injuries should not be treated through a purely physical lens. Furthermore, aligning the themes to the common-sense model provided a conceptual framework conveying the inter-related, emergent nature of the identified themes. The framework provides clinicians with a means to understanding fear after an ACL injury. This could guide assessment and patient education.

An Investigation of the Nature of Fear within ACL-Injured Subjects When Exposed to Provocative Videos: A Concurrent Qualitative and Quantitative Study.

Fear is a factor contributing to poor return to sport after an anterior cruciate (ACL) injury, however the identification and assessment of fear is challenging. To improve understanding of fear, this study qualitatively and quantitatively assessed responses to videos depicting threat to knee stability in people who had experienced an ACL injury. ACL-injured participants who had above average fear on the Tampa Scale of Kinesiophobia and were at least 1-year post-injury/surgery were eligible. Participants were shown four videos depicting sequentially increasing threat to their knee stability (running, cut-and-pivot, feigned knee injury during cut-and-pivot, series of traumatic knee injuries). Qualitative interviews explored participants feeling related to viewing the videos. Participants quantitatively self-rated fear and distress in response to each video. Seventeen participants were included in this study (71% female, with an average time since last ACL injury of 5 ½ years). Five themes were identified: (1) Evoked physiological responses, (2) Deeper contextualisation of the meaning of an ACL injury influencing bodily confidence, (3) Recall of psychological difficulties, (4) Negative implications of a re-injury, and (5) Change to athletic identity. Quantitatively, direct proportionality was noticed between threat level and reported fear and distress. Specifically, participants reported increasing levels of fear and distress as the videos progressed in threat level, with the largest increase seen between a cut-and-pivot movement to a feigned injury during a cut and pivot. The results support the notion that in addition to being a physical injury, an ACL injury has more complex neurophysiological, psychological, and social characteristics which should be considered in management. Using video exposure in the clinic may assist identification of underlying psychological barriers to recovery following an ACL injury, facilitating person-centred care.

Fast and effective removal of contamination from scalp electrical recordings.

OBJECTIVE: To present a new, automated and fast artefact-removal approach which significantly reduces the effect of contamination in scalp electrical recordings. METHOD: We used spectral and temporal characteristics of different sources recorded during a typical scalp electrical recording in order to improve a fast and effective artefact removal approach. Our experiments show that correlation coefficient and spectral gradient of brain components differ from artefactual components. We trained two binary support vector machine classifiers such that one separates brain components from muscle components, and the other separates brain components from mains power and environmental components. We compared the performance of the proposed approach with seven currently used alternatives on three datasets, measuring mains power artefact reduction, muscle artefact reduction and retention of brain neurophysiological responses. RESULTS: The proposed approach significantly reduces the main power and muscle contamination from scalp electrical recording without affecting brain neurophysiological responses. None of the competitors outperformed the new approach. CONCLUSIONS: The proposed approach is the best choice for artefact reduction of scalp electrical recordings. Further improvements are possible with improved component analysis algorithms. SIGNIFICANCE: This paper provides a definitive answer to an important question: Which artefact removal algorithm should be used on scalp electrical recordings?

The effect of Sustained Lung Inflation on Outcomes of Acute Respiratory Distress Syndrome in Preterm Infants Born in Shahid Sadoughi Hospital during 2018.

Background:Respiratory distress syndrome is the chief reason of death in infants. Sustained lung inflation (SLI) may improve respiratory outcomes and reduce the demand for mechanical ventilation (MV). Given that only few studies have been done in this field so far, the current study aimed to evaluate the effect of SLI on outcomes of acute respiratory distress syndrome in preterm infants. Materials and methods:This randomized trial was conducted on preterm infants with respiratory distress syndrome in Shahid Sadoughi Hospital, Yazd, Iran, during 2018. Data were extracted from medical records. Infants born at 25-30 weeks of gestation were randomized into two groups with an equal number of subjects (n=30) in each one. In group 1 (cases), patients received SLI (25 cm H2O for 15 seconds) and nasal continuous positive airway pressure (nCPAP) (5 cm H2O) after oropharynx and nasal suction. In group 2 (controls), patients received only nCPAP (5 cm H2O). Both nCPAP and SLI and were delivered through a T-piece ventilator and neonatal mask. Results:In the current study, no serious differences were seen between case and control groups in terms of either quantitative parameters, including MV duration, Apgar score in the first minute, duration of oxygen therapy, gestational age, birth weight, nCPAP duration, and duration of hospitalization in NICU (P>0.05), or qualitative variables, including sex, pneumothorax rate, rate of intraventricular hemorrhage, need for mechanical ventilation during the first 72 hours of life, surfactant need, and mortality rate (p>0.05), except in cases of complications (p=0.019). Conclusions:According to the results of the current study, neither nCPAP alone, nor SLI and nCPAP had any effect on the duration, or need, or type of mechanical ventilation, while the incidence of complications in the nCPAP alone group (control group) was higher than that of combined nCPAP + SLI group (case group). It is suggested that future studies should be conducted on a larger sample size.

Detecting connectivity in EEG: A comparative study of data-driven effective connectivity measures.

In this paper, we perform the first comparison of a large variety of effective connectivity measures in detecting causal effects among observed interacting systems based on their statistical significance. Well-known measures estimating direction and strength of interdependence between time series are compared: information theoretic measures, model-based multivariate measures in the time and frequency domains, and phase-based measures. The performance of measures is tested on simulated data from three systems: three coupled Hénon maps; a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and simulated EEG. No measure was consistently superior. Measures that model the data as MVAR perform well when the data are drawn from that model. Frequency domain measures perform well when the data have a clearly defined band of interest. When neither of these is true, information theoretic measures perform well. Overall, the measure with the best performance in a variety of situations and with a low computational cost is conditional Granger causality. Partial Granger causality and multivariate Granger causality are also good measures, but their computational cost rises rapidly with the number of channels. Copula Granger causality can also be used reliably, but its computational cost rises rapidly with the number of data.

Detecting synchrony in EEG: A comparative study of functional connectivity measures.

In neuroscience, there is considerable current interest in investigating the connections between different parts of the brain. EEG is one modality for examining brain function, with advantages such as high temporal resolution and low cost. Many measures of connectivity have been proposed, but which is the best measure to use? In this paper, we address part of this question: which measure is best able to detect connections that do exist, in the challenging situation of non-stationary and noisy data from nonlinear systems, like EEG. This requires knowledge of the true relationship between signals, hence we compare 26 measures of functional connectivity on simulated data (unidirectionally coupled Hénon maps, and simulated EEG). To determine whether synchrony is detected, surrogate data were generated and analysed, and a threshold determined from the surrogate ensemble. No measure performed best in all tested situations. The correlation and coherence measures performed best on stationary data with many samples. S-estimator, correntropy, mean-phase coherence (Hilbert), mutual information (kernel), nonlinear interdependence (S) and nonlinear interdependence (N) performed most reliably on non-stationary data with small to medium window sizes. Of these, correlation and S-estimator have execution times that scale slower with the number of channels and the number of samples.

Resting cranial and upper cervical muscle activity is increased in patients with migraine.

OBJECTIVE: To compare comprehensive measures of scalp-recorded muscle activity in migraineurs and controls. METHOD: We used whole-of-head high-density scalp electrical recordings, independent component analysis (ICA) and spectral slope of the derived components, to define muscle (electromyogram-containing) components. After projecting muscle components back to scalp, we quantified scalp spectral power in the frequency range, 52-98 Hz, reflecting muscle activation. We compared healthy subjects (n = 65) and migraineurs during a non-headache period (n = 26). We also examined effects due to migraine severity, gender, scalp-region and task (eyes-closed and eyes-open). We could not examine the effect of pre-ictal versus inter-ictal versus post-ictal as this information was not available in the pre-existing dataset. RESULTS: There was more power due to muscle activity (mean ±â€¯SEM) in migraineurs than controls (respectively, -13.61 ±â€¯0.44 dB versus -14.73 ±â€¯0.24 dB, p = 0.028). Linear regression showed no relationship between headache frequency and muscle activity in any combination of region and task. There was more power during eyes-open than eyes-closed (respectively, -13.42 ±â€¯0.34 dB versus -14.92 ±â€¯0.34 dB, p = 0.002). CONCLUSIONS: There is an increase in cranial and upper cervical muscle activity in non-ictal migraineurs versus controls. This raises questions of the role of muscle in migraine, and the possible differentiation of non-ictal phases. SIGNIFICANCE: This provides preliminary evidence to date of possible cranial muscle involvement in migraine.

Improved artefact removal from EEG using Canonical Correlation Analysis and spectral slope.

BACKGROUND: Contamination of scalp measurement by tonic muscle artefacts, even in resting positions, is an unavoidable issue in EEG recording. These artefacts add significant energy to the recorded signals, particularly at high frequencies. To enable reliable interpretation of subcortical brain activity, it is necessary to detect and discard this contamination. NEW METHOD: We introduce a new automatic muscle-removal approach based on the traditional Blind Source Separation-Canonical Correlation Analysis (BSS-CCA) method and the spectral slope of its components. We show that CCA-based muscle-removal methods can discriminate between signals with high correlation coefficients (brain, mains artefact) and signals with low correlation coefficients (white noise, muscle). We also show that typical BSS-CCA components are not purely from one source, but are mixtures from multiple sources, limiting the performance of BSS-CCA in artefact removal. We demonstrate, using our paralysis dataset, improved performance using BSS-CCA followed by spectral-slope rejection. RESULT: This muscle removal approach can reduce high-frequency muscle contamination of EEG, especially at peripheral channels, while preserving steady-state brain responses in cognitive tasks. COMPARISON WITH EXISTING METHODS: This approach is automatic and can be applied on any sample of data easily. The results show its performance is comparable with the ICA method in removing muscle contamination and has significantly lower computational complexity. CONCLUSION: We identify limitations of the traditional BSS-CCA approach to artefact removal in EEG, propose and test an extension based on spectral slope that makes it automatic and improves its performance, and results in performance comparable to competitors such as ICA-based artefact removal.

Evaluation of a minimum-norm based beamforming technique, sLORETA, for reducing tonic muscle contamination of EEG at sensor level.

BACKGROUND: Cranial and cervical muscle activity (electromyogram, EMG) contaminates the surface electroencephalogram (EEG) from frequencies below 20 through to frequencies above 100Hz. It is not possible to have a reliable measure of cognitive tasks expressed in EEG at gamma-band frequencies until the muscle contamination is removed. NEW METHOD: In the present work, we introduce a new approach of using a minimum-norm based beamforming technique (sLORETA) to reduce tonic muscle contamination at sensor level. Using a generic volume conduction model of the head, which includes three layers (brain, skull, and scalp), and sLORETA, we estimated time-series of sources distributed within the brain and scalp. The sources within the scalp were considered to be muscle and discarded in forward modelling. RESULT: (1) The method reduced EMG contamination, more strongly at peripheral channels; (2) task-induced cortical activity was retained or revealed after removing putative muscle activity. COMPARISON WITH EXISTING METHODS: This approach can decrease tonic muscle contamination in scalp measurements without relying on time-consuming processing of expensive MRI data. In addition, it is competitive to ICA in muscle reduction and can be reliably applied on any length of recorded data that captures the dynamics of the signals of interest. CONCLUSION: This study suggests that sLORETA can be used as a method to quantitate cranial muscle activity and reduce its contamination at sensor level.