Assessment of visibility graph similarity as a synchronization measure for chaotic, noisy and stochastic time series

\u3cp\u3eFinding synchronization between the outputs of a dynamic system, which are represented mostly as time series, helps to characterize the system activities during an occurrence. An important issue in analyzing time series is that they may behave chaotically or stochastically. Therefore, applying a reliable synchronization measure which can capture the dynamic features of the system helps to quantify the interdependencies between time series, correctly. In this paper, we employ similarity measures based on visibility graph (VG) algorithms as an alternative and radically different way to measure the synchronization between time series. We assess the performance of VG-based similarity measures on chaotic, noisy and stochastic time series. In our experiments, we use the Rössler system and the noisy Hénon map as representative instances of chaotic systems, and the Kuramoto model for studying detection of synchronization between stochastic time series. Our study suggests that the similarity measure based on the horizontal VG algorithm should be favored to other measures for detecting synchronization between chaotic and stochastic time series.\u3c/p\u3

Speckle-initialized dynamic segmentation of the prostate

Echography is a commonly used modality for prostate imaging. Prostate segmentation is the first step in analyzing echographic prostate images. Because of the nature of these images, traditional local image processing operators are inadequate for finding the prostate boundary. Most automated segmentations described in literature require user interaction for contour initializing or editing. Also shape templates are applied as prior knowledge. In this paper, an automatic segmentation method is presented, based on prostate specific image granulation and image intensity. First, a granulation detector is used to extract granulation. Subsequently, the Hessian is adopted to evaluate granulation shape and intensity for the extraction of the prostate-specific dot pattern. This dot pattern is used to construct the contour initialization. A smooth contour model (discrete dynamic contour; DDC) is evolved from this initialization to the final contour. The guiding vector field for the DDC deformation is the gradient vector flow field calculated from an edge map of the original image. The scale of the relevant edges (large compared to granulation) is estimated from the prostate-specific dot pattern. Comparison of automated segmentations with clinical expert manual segmentations reveals a mean sensitivity and accuracy of 0.90 and 0.93, respectivel

Neu\u3csup\u3e3\u3c/sup\u3eCA-RT:a framework for real-time fMRI analysis

\u3cp\u3eReal-time functional magnetic resonance imaging (rtfMRI) allows visualisation of ongoing brain activity of the subject in the scanner. Denoising algorithms aim to rid acquired data of confounding effects, enhancing the blood oxygenation level-dependent (BOLD) signal. Further image processing and analysis methods, like general linear models (GLM) or multivariate analysis, then present application-specific information to the researcher. These processes are typically applied to regions of interest but, increasingly, rtfMRI techniques extract and classify whole brain functional networks and dynamics as correlates for brain states or behaviour, particularly in neuropsychiatric and neurocognitive disorders. We present Neu\u3csup\u3e3\u3c/sup\u3eCA-RT: a Matlab-based rtfMRI analysis framework aiming to advance scientific knowledge on real-time cognitive brain activity and to promote its translation into clinical practice. Design considerations are listed based on reviewing existing rtfMRI approaches. The toolbox integrates established SPM preprocessing routines, real-time GLM mapping of fMRI data to a basis set of spatial brain networks, correlation of activity with 50 behavioural profiles from the BrainMap database, and an intuitive user interface. The toolbox is demonstrated in a task-based experiment where a subject executes visual, auditory and motor tasks inside a scanner. In three out of four experiments, resulting behavioural profiles agreed with the expected brain state.\u3c/p\u3

Wavelet entropy of BOLD time series:an application to Rolandic epilepsy

\u3cp\u3ePurpose: To assess the wavelet entropy for the characterization of intrinsic aberrant temporal irregularities in the time series of resting-state blood-oxygen-level-dependent (BOLD) signal fluctuations. Further, to evaluate the temporal irregularities (disorder/order) on a voxel-by-voxel basis in the brains of children with Rolandic epilepsy. Materials and Methods: The BOLD time series was decomposed using the discrete wavelet transform and the wavelet entropy was calculated. Using a model time series consisting of multiple harmonics and nonstationary components, the wavelet entropy was compared with Shannon and spectral (Fourier-based) entropy. As an application, the wavelet entropy in 22 children with Rolandic epilepsy was compared to 22 age-matched healthy controls. The images were obtained by performing resting-state functional magnetic resonance imaging (fMRI) using a 3T system, an 8-element receive-only head coil, and an echo planar imaging pulse sequence (T\u3csub\u3e2\u3c/sub\u3e \u3csup\u3e*\u3c/sup\u3e -weighted). The wavelet entropy was also compared to spectral entropy, regional homogeneity, and Shannon entropy. Results: Wavelet entropy was found to identify the nonstationary components of the model time series. In Rolandic epilepsy patients, a significantly elevated wavelet entropy was observed relative to controls for the whole cerebrum (P = 0.03). Spectral entropy (P = 0.41), regional homogeneity (P = 0.52), and Shannon entropy (P = 0.32) did not reveal significant differences. Conclusion: The wavelet entropy measure appeared more sensitive to detect abnormalities in cerebral fluctuations represented by nonstationary effects in the BOLD time series than more conventional measures. This effect was observed in the model time series as well as in Rolandic epilepsy. These observations suggest that the brains of children with Rolandic epilepsy exhibit stronger nonstationary temporal signal fluctuations than controls. Level of Evidence: 2. Technical Efficacy: Stage 3. J. Magn. Reson. Imaging 2017;46:1728–1737.\u3c/p\u3

Functional network abnormalities consistent with behavioral profile in autism spectrum disorder

\u3cp\u3eAutism spectrum disorder (ASD) is a neurodevelopmental disorder in which the severity of symptoms varies over subjects. The iCAPs model (innovation-driven co-activation patterns) is a recently developed spatio-temporal model to describe fMRI data. In this study, the iCAPs model was employed to find functional imaging biomarkers for ASD in resting-state fMRI data. MRI data from 125 ASD patients and 243 healthy controls was selected from the online ABIDE data repository. Following standard fMRI preprocessing steps, the iCAP patterns were fitted to the data to obtain network time series. Furthermore, specific combinations of iCAPs were mapped to behavioral domain time series. To quantify to which extent the time series contribute to the fMRI dynamics, their (temporal) standard deviation was calculated and compared between patients and controls. Abnormalities were found in networks involving subcortical and limbic areas and default mode network regions. When mapping the network dynamics to behavioral domain time series, abnormalities were found in emotional and visual behavioral subdomains, and within the ASD spectrum were more pronounced in subjects with autism compared to Asperger's syndrome. Also a trend towards impairment in networks facilitating social cognition was found. The functional imaging abnormalities are consistent with the behavioral impairments typical for ASD.\u3c/p\u3

Delayed convergence between brain network structure and function in rolandic epilepsy

\u3cp\u3eINTRODUCTION: Rolandic epilepsy (RE) manifests during a critical phase of brain development, and has been associated with language impairments. Concordant abnormalities in structural and functional connectivity (SC and FC) have been described before. As SC and FC are under mutual influence, the current study investigates abnormalities in the SC-FC synergy in RE.\u3c/p\u3e\u3cp\u3eMETHODS: Twenty-two children with RE (age, mean ± SD: 11.3 ± 2.0 y) and 22 healthy controls (age 10.5 ± 1.6 y) underwent structural, diffusion weighted, and resting-state functional magnetic resonance imaging (MRI) at 3T. The probabilistic anatomical landmarks atlas was used to parcellate the (sub)cortical gray matter. Constrained spherical deconvolution tractography and correlation of time series were used to assess SC and FC, respectively. The SC-FC correlation was assessed as a function of age for the non-zero structural connections over a range of sparsity values (0.01-0.75). A modularity analysis was performed on the mean SC network of the controls to localize potential global effects to subnetworks. SC and FC were also assessed separately using graph analysis.\u3c/p\u3e\u3cp\u3eRESULTS: The SC-FC correlation was significantly reduced in children with RE compared to healthy controls, especially for the youngest participants. This effect was most pronounced in a left and a right centro-temporal network, as well as in a medial parietal network. Graph analysis revealed no prominent abnormalities in SC or FC network organization.\u3c/p\u3e\u3cp\u3eCONCLUSION: Since SC and FC converge during normal maturation, our finding of reduced SC-FC correlation illustrates impaired synergy between brain structure and function. More specifically, since this effect was most pronounced in the youngest participants, RE may represent a developmental disorder of delayed brain network maturation. The observed effects seem especially attributable to medial parietal connections, which forms an intermediate between bilateral centro-temporal modules of epileptiform activity, and bear relevance for language function.\u3c/p\u3

Focal application of accelerated iTBS results in global changes in graph measures

\u3cp\u3eGraph analysis was used to study the effects of accelerated intermittent theta burst stimulation (aiTBS) on the brain's network topology in medication-resistant depressed patients. Anatomical and resting-state functional MRI (rs-fMRI) was recorded at baseline and after sham and verum stimulation. Depression severity was assessed using the Hamilton Depression Rating Scale (HDRS). Using various graph measures, the different effects of sham and verum aiTBS were calculated. It was also investigated whether changes in graph measures were correlated to clinical responses. Furthermore, by correlating baseline graph measures with the changes in HDRS in terms of percentage, the potential of graph measures as biomarker was studied. Although no differences were observed between the effects of verum and sham stimulation on whole-brain graph measures and changes in graph measures did not correlate with clinical response, the baseline values of clustering coefficient and global efficiency showed to be predictive of the clinical response to verum aiTBS. Nodal effects were found throughout the whole brain. The distribution of these effects could not be linked to the strength of the functional connectivity between the stimulation site and the node. This study showed that the effects of aiTBS on graph measures distribute beyond the actual stimulation site. However, additional research into the complex interactions between different areas in the brain is necessary to understand the effects of aiTBS in more detail.\u3c/p\u3

Cognitive deterioration in adult epilepsy:does accelerated cognitive ageing exist?

\u3cp\u3eA long-standing concern has been whether epilepsy contributes to cognitive decline or so-called 'epileptic dementia'. Although global cognitive decline is generally reported in the context of chronic refractory epilepsy, it is largely unknown what percentage of patients is at risk for decline. This review is focused on the identification of risk factors and characterization of aberrant cognitive trajectories in epilepsy. Evidence is found that the cognitive trajectory of patients with epilepsy over time differs from processes of cognitive ageing in healthy people, especially in adulthood-onset epilepsy. Cognitive deterioration in these patients seems to develop in a 'second hit model' and occurs when epilepsy hits on a brain that is already vulnerable or vice versa when comorbid problems develop in a person with epilepsy. Processes of ageing may be accelerated due to loss of brain plasticity and cognitive reserve capacity for which we coin the term 'accelerated cognitive ageing'. We believe that the concept of accelerated cognitive ageing can be helpful in providing a framework understanding global cognitive deterioration in epilepsy.\u3c/p\u3

Cognitive deterioration in adult epilepsy : clinical characteristics of “accelerated cognitive ageing

Objectives\u3cbr/\u3e“Epileptic dementia” is reported in adults with childhood-onset refractory epilepsy. Cognitive deterioration can also occur in a “second-hit model”.\u3cbr/\u3e\u3cbr/\u3eMaterials and methods\u3cbr/\u3eWe studied the clinical and neuropsychological characteristics of patients with cognitive deterioration (≥1 SD discrepancy between current IQ and premorbid IQ). Memory function, reaction time and processing speed were also evaluated. Analyses were performed to investigate which clinical characteristics correlated with cognitive deterioration.\u3cbr/\u3e\u3cbr/\u3eResults\u3cbr/\u3eTwenty-seven patients were included with a mean age of 55.7 years old, an average age at epilepsy onset of 33.9 years and a mean duration of 21.8 years. Over 40% had experienced at least one status epilepticus. About 77.8% had at least one comorbid disease (most of (cardio)vascular origin). Cognitive deterioration scores were significant for both Performance IQ and Full Scale IQ, but not for Verbal IQ. Impairments in fluid functions primarily affected the IQ-scores. Memory was not impaired. Epilepsy factors explained 7% of the variance in deterioration, whereas 38% was explained by relatively low premorbid IQ and educational level, high age at seizure onset and older age.\u3cbr/\u3e\u3cbr/\u3eConclusions\u3cbr/\u3eA subgroup of patients with localization-related epilepsy exhibits cognitive decline characterized by deterioration in PIQ and FSIQ, but with preserved higher order functions (VIQ and memory). Patients typically have epilepsia tarda, comorbid pathology, relatively low educational level and older age. These are factors known to increase the vulnerability of the brain by diminishing cognitive reserve. Cognitive deterioration may develop according to a stepwise “second-hit model”, affecting and accelerating the cognitive ageing process.\u3cbr/\u3