This study demonstrated that quantitative analysis of the echo intensity of US images can provide important information. However, further research is necessary to explore the relationships among sex, age, blob area, count, body mass index, regional anatomy, and extent of training or exercise of the particular muscle.
Purpose Ultrasound is a non-invasive quantitative method to characterize sonographic textures of skeletal muscles. To date, there is no information available on the trapezius muscle. This study assessed the trapezius muscles of patients with myofascial pain compared with normal healthy participants. Methods The trapezius muscles of 15 healthy and 17 myofascial pain participants were assessed using B-mode ultrasound to obtain 120 images for healthy and 162 images from myofascial pain participants. Texture features such as blob area, count and local binary patterns (LBP) were calculated. Multi-feature classification and analysis were performed using principal component analysis (PCA) and MANOVA to determine whether there were statistical differences. ResultsWe demonstrate the two principal components composed of a combination of LBP and blob parameters which explain 92.55% of the cumulative variance of our data set. In addition, blob characteristics were significantly different between healthy and myofascial pain participants. ConclusionOur study provides evidence that texture analysis techniques can differentiate between healthy and myofascial pain affected trapezius muscles. Further research is necessary to evaluate the nature of these differences. Keywords Ultrasound • Myofascial pain • Trapezius muscle • Quantitation • Texture analysis SommarioObiettivi L'ecografia è un metodo quantitativo non invasivo utile per caratterizzare la texture sonografica dei muscoli scheletrici. Allo stato attuale, vi sono pochi dati in letteratura riguardo il muscolo trapezio. Questo studio ha valutato i muscoli trapezi dei pazienti con dolore miofasciale confrontandoli con quelli dei soggetti sani. Metodi L'ecografia B-mode è stata utilizzata per valutare i muscoli trapezi di 15 soggetti sani e di 17 pazienti con dolore miofasciale , ottenendo 120 immagini per i soggetti sani e 162 per i pazienti con dolore miofasciale. Sono state calcolate caratteristiche di texture come la blob area, i count ed i local binary pattern (LBP). La classificazione e l'analisi multiparametrica sono state eseguite utilizzando l'analisi delle componenti principali (PCA) e MANOVA per valutare se vi fossero differenze statistiche. Risultati Abbiamo dimostrato che due componenti principali, composte da una combinazione di LBP e parametri blob, spiegano il 92.55% della varianza cumulativa dei nostri dati. Inoltre, le caratteristiche blob erano significativamente differenti tra i pazienti con dolore miofasciale ed i soggetti sani. Conclusioni Il nostro studio fornisce l'evidenza che le tecniche di texture analysis possono differenziare i muscoli trapezi dei soggetti sani da quelli dei pazienti affetti da dolore miofasciale. Ulteriori studi sono necessari per valutare la natura di tali differenze.
The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.
Functional magnetic resonance imaging (fMRI) has identified dysfunctional network dynamics underlying a number of psychopathologies, including post-traumatic stress disorder, depression and schizophrenia. There is tremendous potential for the development of network-based clinical biomarkers to better characterize these disorders. However, to realize this potential requires the ability to track brain networks using a more affordable imaging modality, such as Electroencephalography (EEG). Here we present a novel analysis pipeline capable of tracking brain networks from EEG alone, after training on supervisory signals derived from data simultaneously recorded in EEG and fMRI, while people engaged in various cognitive tasks. EEG-based features were then used to classify three cognitively-relevant brain networks with up to 75\% accuracy. These findings could lead to affordable and non-invasive methods to objectively diagnose brain disorders involving dysfunctional network dynamics, and to track and even predict treatment responses.
The highly influential tri-network model proposed by Menon integrates 3 key intrinsic brain networks --- the central executive network (CEN), salience network (SN), and the default mode network (DMN), into a single cohesive model underlying normal behaviour and cognition. A large body of evidence suggests that abnormal intra- and inter- network connectivity between these three networks underlies the various behavioural and cognitive dysfunctions observed in patients with neuropsychiatric conditions such as PTSD and depression. An important prediction of the tri-network model is that the DMN and CEN networks are anti-correlated under the control of the SN, such that if a task engages one of the two, the SN inhibits the activation of the other. To date most of the evidence surrounding the functions of these three core networks comes from either resting state analyses or in the context of a single task with respect to rest. Few studies have investigated multiple tasks simultaneously or characterized the dynamics of task switching. Hence, a careful investigation of the temporal dynamics of network activity during task switching is warranted. To accomplish this we collected fMRI data from 14 participants that dynamically switched between a 2-back working memory task and an autobiographical memory retrieval task, designed to activate the CEN, DMN and the SN. The fMRI data were used to 1. identify nodes and sub-networks within the three major networks involved in task-linked dynamic network switching, 2. characterize the temporal pattern of activation of these nodes and sub-networks, and finally 3. investigate the causal influence that these nodes and sub-networks exerted on each other. Using a combination of multivariate neuroimaging analyses, timecourse analyses and multivariate Granger causality measures to study the tri-network dynamics, the current study found that the SN co-activates with the task-relevant network, providing a mechanistic insight into SN-mediated network selection in the context of explicit tasks. Our findings also indicate active involvement of the posterior insula and some medial temporal nodes in task-linked functions of the SN and DMN, warranting their inclusion as network nodes in future studies of the tri-network model. These results add to the growing body of evidence showing the complex interplay of CEN, DMN and SN nodes and sub-networks required for adequate task-switching, and characterizes a normative pattern of task-linked network dynamics within the context of Menon's tri-network model.
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