Analyzing spatio-temporal patterns of genuine cross-correlations

In multivariate time series analysis, the equal-time cross-correlation is a classic and computationally efficient measure for quantifying linear interrelations between data channels. When the cross-correlation coefficient is estimated using a finite amount of data points, its non-random part may be strongly contaminated by a sizable random contribution, such that no reliable conclusion can be drawn about genuine mutual interdependencies. The random correlations are determined by the signals' frequency content and the amount of data points used. Here, we introduce adjusted correlation matrices that can be employed to disentangle random from non-random contributions to each matrix element independently of the signal frequencies. Extending our previous work these matrices allow analyzing spatial patterns of genuine cross-correlation in multivariate data regardless of confounding influences. The performance is illustrated by example of model systems with known interdependence patterns. Finally, we apply the methods to electroencephalographic (EEG) data with epileptic seizure activity.

A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations

Synaptic strength depresses for low and potentiates for high activation of the postsynaptic neuron. This feature is a key property of the Bienenstock–Cooper–Munro (BCM) synaptic learning rule, which has been shown to maximize the selectivity of the postsynaptic neuron, and thereby offers a possible explanation for experience-dependent cortical plasticity such as orientation selectivity. However, the BCM framework is rate-based and a significant amount of recent work has shown that synaptic plasticity also depends on the precise timing of presynaptic and postsynaptic spikes. Here we consider a triplet model of spike-timing–dependent plasticity (STDP) that depends on the interactions of three precisely timed spikes. Triplet STDP has been shown to describe plasticity experiments that the classical STDP rule, based on pairs of spikes, has failed to capture. In the case of rate-based patterns, we show a tight correspondence between the triplet STDP rule and the BCM rule. We analytically demonstrate the selectivity property of the triplet STDP rule for orthogonal inputs and perform numerical simulations for nonorthogonal inputs. Moreover, in contrast to BCM, we show that triplet STDP can also induce selectivity for input patterns consisting of higher-order spatiotemporal correlations, which exist in natural stimuli and have been measured in the brain. We show that this sensitivity to higher-order correlations can be used to develop direction and speed selectivity.

Genuine cross-correlations: Which surrogate based measure reproduces analytical results best?

The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.

Developmental changes of BOLD signal correlations with global human EEG power and synchronization during working memory

In humans, theta band (5-7 Hz) power typically increases when performing cognitively demanding working memory (WM) tasks, and simultaneous EEG-fMRI recordings have revealed an inverse relationship between theta power and the BOLD (blood oxygen level dependent) signal in the default mode network during WM. However, synchronization also plays a fundamental role in cognitive processing, and the level of theta and higher frequency band synchronization is modulated during WM. Yet, little is known about the link between BOLD, EEG power, and EEG synchronization during WM, and how these measures develop with human brain maturation or relate to behavioral changes. We examined EEG-BOLD signal correlations from 18 young adults and 15 school-aged children for age-dependent effects during a load-modulated Sternberg WM task. Frontal load (in-)dependent EEG theta power was significantly enhanced in children compared to adults, while adults showed stronger fMRI load effects. Children demonstrated a stronger negative correlation between global theta power and the BOLD signal in the default mode network relative to adults. Therefore, we conclude that theta power mediates the suppression of a task-irrelevant network. We further conclude that children suppress this network even more than adults, probably from an increased level of task-preparedness to compensate for not fully mature cognitive functions, reflected in lower response accuracy and increased reaction time. In contrast to power, correlations between instantaneous theta global field synchronization and the BOLD signal were exclusively positive in both age groups but only significant in adults in the frontal-parietal and posterior cingulate cortices. Furthermore, theta synchronization was weaker in children and was--in contrast to EEG power--positively correlated with response accuracy in both age groups. In summary we conclude that theta EEG-BOLD signal correlations differ between spectral power and synchronization and that these opposite correlations with different distributions undergo similar and significant neuronal developments with brain maturation.

Observation of Associated Near-Side and Away-Side Long-Range Correlations in √sNN=5.02 TeV Proton-Lead Collisions with the ATLAS Detector

Two-particle correlations in relative azimuthal angle (Delta phi) and pseudorapidity (Delta eta) are measured in root S-NN = 5.02 TeV p + Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1 mu b(-1) of data as a function of transverse momentum (p(T)) and the transverse energy (Sigma E-T(Pb)) summed over 3.1 < eta < 4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2 < vertical bar Delta eta vertical bar < 5) "near-side" (Delta phi similar to 0) correlation that grows rapidly with increasing Sigma E-T(Pb). A long-range "away-side" (Delta phi similar to pi) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small Sigma E-T(Pb), is found to match the near-side correlation in magnitude, shape (in Delta eta and Delta phi) and Sigma E-T(Pb) dependence. The resultant Delta phi correlation is approximately symmetric about pi/2, and is consistent with a dominant cos2 Delta phi modulation for all Sigma E-T(Pb) ranges and particle p(T).

Clinicopathologic correlations of silent corticotroph adenomas of the pituitary: report of four cases and literature review

Silent corticotroph adenomas (SCA) are rare pituitary tumors with histologic hallmarks of corticotroph differentiation, including ACTH immunoreactivity, but lacking clinical evidence of Cushing's syndrome. We report on four female patients, aged 19-66 years, each presenting with a nonfunctional macroadenoma. Leading symptoms were headache in two cases and visual field deficits in one. One patient was incidentally diagnosed while undergoing cranial MRI for an unrelated condition. Three patients had marked obesity; none of them presented constitutional signs of Cushing's syndrome. Serum cortisol levels were moderately elevated in the two patients systematically tested in this respect. Marginal to moderate hyperprolactinemia was present in two cases. Two patients also were shown to be deficient in either gonadotroph or thyrotroph axis, while a third had a combined insufficiency of both gonadotroph and thyrotroph axis. MRI scans revealed intratumoral hemorrhage and/or cystic change in three cases, as well as tumor-related occlusive hydrocephalus in one. The latter patient was biopsied only, while the remaining underwent gross total resection. Histologically, all four lesions were diagnosed as SCA subtype I displaying intense immunoreactivity for ACTH. In three tumors, scattered cells coexpressed PRL as well. In addition, Crooke's hyaline change was noted in a significant number of tumor cells and in residual non-neoplastic corticotrophs in one case each. With MIB-1 labeling indices of 1-3%, none of the tumors qualified as atypical adenoma. We conclude that SCAs are more likely to be discovered as expansile tumors, whose advanced local space-occupying character at surgery rather than an inherently aggressive growth potential may negatively influence the clinical outcome. Subtle morphologic evidence of corticotroph suppression in residual pituitary adjacent to tumor lends further support to literature data indicating minimal or intermittent functional activity in this tumor type.

Asymptotics for Marginal Generalized Linear Models With Sparse Correlations

Marginal generalized linear models can be used for clustered and longitudinal data by fitting a model as if the data were independent and using an empirical estimator of parameter standard errors. We extend this approach to data where the number of observations correlated with a given one grows with sample size and show that parameter estimates are consistent and asymptotically Normal with a slower convergence rate than for independent data, and that an information sandwich variance estimator is consistent. We present two problems that motivated this work, the modelling of patterns of HIV genetic variation and the behavior of clustered data estimators when clusters are large.