An efficient implementation of the synchronization likelihood algorithm for functional connectivity

Measures of functional connectivity are commonly employed in neuroimaging research. Among the most popular measures is the Synchronization Likelihood which provides a non-linear estimate of the statistical dependencies between the activity time courses of different brain areas. One aspect which has limited a wider use of this algorithm is the fact that it is very computationally and memory demanding. In the present work we propose new implementations and parallelizations of the Synchronization Likelihood algorithm with significantly better performance both in time and in memory use. As a result both the amount of required computational time is reduced by 3 orders of magnitude and the amount of memory needed for calculations is reduced by 2 orders of magnitude. This allows performing analyses that were not feasible before from a computational standpoint

An efficient implementation of the synchronization likelihood algorithm for functional connectivity

Measures of functional connectivity are commonly employed in neuroimaging research. Among the most popular measures is the Synchronization Likelihood which provides a non-linear estimate of the statistical dependencies between the activity time courses of different brain areas. One aspect which has limited a wider use of this algorithm is the fact that it is very computationally and memory demanding. In the present work we propose new implementations and parallelizations of the Synchronization Likelihood algorithm with significantly better performance both in time and in memory use. As a result both the amount of required computational time is reduced by 3 orders of magnitude and the amount of memory needed for calculations is reduced by 2 orders of magnitude. This allows performing analyses that were not feasible before from a computational standpoint

Signal-to-noise ratio of the MEG signal after preprocessing

Background Magnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise. New method The effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials. Results Recordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5–10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI. Comparison with existing methods No previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed. Conclusions The application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable

Central pain augmentation in fibromyalgia during subjectively-matched mechanical stimulation

The precise pathophysiology of fibromyalgia, a syndrome characterized by, among other symptoms, chronic widespread pain, remains to be elucidated (Abeles et al., 2007). The fact that, when subjected to the same amount of stimulation, patients show enhanced brain responses as compared to controls provides evidence of central pain augmentation in this syndrome. We aimed to characterize brain response differences when stimulation is adjusted to elicit similar subjective levels of pain in both groups

Functional connectivity in mild cognitive impairment during a memory task: implications for the disconnection hypothesis.

Mild cognitive impairment (MCI) has been considered an intermediate state between healthy aging and dementia. The early damage in anatomical connectivity and progressive loss of synapses that characterize early Alzheimer's disease suggest that MCI could also be a disconnection syndrome. Here, we compare the degree of synchronization of brain signals recorded with magnetoencephalography from patients (22) with MCI with that of healthy controls (19) during a memory task. Synchronization Likelihood, an index based on the theory of nonlinear dynamical systems, was used to measure functional connectivity. During the memory task patients showed higher interhemispheric synchronization than healthy controls between left and right -anterior temporo-frontal regions (in all studied frequency bands) and in posterior regions in the γ band. On the other hand, the connectivity pattern from healthy controls indicated two clusters of higher synchronization, one among left temporal sensors and another one among central channels. Both of them were found in all frequency bands. In the γ band, controls showed higher Synchronization Likelihood values than MCI patients between central-posterior and frontal-posterior channels and a high synchronization in posterior regions. The inter-hemispheric increased synchronization values could reflect a compensatory mechanism for the lack of efficiency of the memory networks in MCI patients. Therefore, these connectivity profiles support only partially the idea of MCI as a disconnection syndrome, as patients showed increased long distance inter-hemispheric connections but a decrease in antero-posterior functional connectivit

Effects on orientation perception of manipulating the spatio–temporal prior probability of stimuli

Spatial and temporal regularities commonly exist in natural visual scenes. The knowledge of the probability structure of these regularities is likely to be informative for an efficient visual system. Here we explored how manipulating the spatio–temporal prior probability of stimuli affects human orientation perception. Stimulus sequences comprised four collinear bars (predictors) which appeared successively towards the foveal region, followed by a target bar with the same or different orientation. Subjects' orientation perception of the foveal target was biased towards the orientation of the predictors when presented in a highly ordered and predictable sequence. The discrimination thresholds were significantly elevated in proportion to increasing prior probabilities of the predictors. Breaking this sequence, by randomising presentation order or presentation duration, decreased the thresholds. These psychophysical observations are consistent with a Bayesian model, suggesting that a predictable spatio–temporal stimulus structure and an increased probability of collinear trials are associated with the increasing prior expectation of collinear events. Our results suggest that statistical spatio–temporal stimulus regularities are effectively integrated by human visual cortex over a range of spatial and temporal positions, thereby systematically affecting perception

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing

Etología. Introducción a la ciencia del comportamiento

1ª ed., 3ª reimp.El objetivo de este libro es proporcionar una introducción a la Etología. Ésta ciencia responde al interés por profundizar en el conocimiento de las costumbres animales; por comprender la variedad de comportamientos que en diferentes situaciones exhiben los individuos de diferentes especies. Una posible definición de Etología podría ser: el estudio científico del comportamiento de los seres vivos. A lo largo de esta obra se analizan todos los aspectos que tienen que ver con el comportamiento animal, desde las causas y mecanismos de actuación o la adaptación y evolución y el bienestar animal.The purpose of this book is to provide an introduction to Ethology. This science responds to the interest in deepening the knowledge of animal customs; to understand the variety of behaviour that in different situations exhibit individuals of different species. A possible definition of ethology could be: the scientific study of the behavior of living beings. Throughout this book all aspects related to animal behavior, from the causes and mechanisms of action or adaptation and evolution and animal welfare, are analyzed

UNR/CDSE1 expression as prognosis biomarker in resectable pancreatic ductal adenocarcinoma patients: A proof-of-concept

Pancreatic ductal adenocarcinoma is an aggressive form of pancreatic cancer and the fourth leading cause of cancer-related death. When possible, curative approaches are based on surgical resection, though not every patient is a candidate for surgery. There are clinical guidelines for the management of these patients that offer different treatment options depending on the clinical and pathologic characteristics. However, the survival rates seen in this kind of patients are still low. The CDSE1 gene is located upstream of NRAS and encodes an RNA-binding protein termed UNR. The aim of this study was to analyze UNR expression and its correlation with outcome in patients with resectable pancreatic ductal adenocarcinoma (PDAC). For this, samples from resectable PDAC patients who underwent duodenopancreatectomy were used to evaluate UNR protein expression by immunohistochemistry using a tissue microarray. Here, we observed that low UNR expression was significantly associated with shorter progression-free survival after surgery (P = 0.010). Moreover, this prognostic marker remained significant after Cox proportional hazards model (P = 0.036). We further studied the role of CDSE1 expression in patient’s prognosis using data from public repositories (GEO and TGCA), confirming our results. Interestingly, CDSE1 expression correlated with that of genes characteristic of an immunogenic molecular subtype of pancreatic cancer. Based on these findings, UNR may be considered a potential prognostic biomarker for resectable PDAC and may serve to guide subsequent adjuvant treatment decisionsThis work has been carried out with the support of the RNA-Reg CONSOLIDER Network CSD2009-00080 (J.M.-U. and J.G.-F.), and Spanish Health Research Project Funds PI16/ 01468 from ªInstituto de Salud Carlos IIIº (A.C. and J.G.-F.), both of the Spanish Ministry of Economy, Industry and Competitivenes

Reorganization of Functional Networks in Mild Cognitive Impairment

Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI) subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG) time series obtained during a memory task were evaluated by synchronization likelihood (SL), to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD), these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD