Perception of causality in schizophrenia spectrum disorder

Patients with schizophrenia spectrum disorders often maintain deviating views on cause-effect relationships, especially when positive and disorganization symptoms are manifest. Altered perceived causality is prominent in delusional ideation, in ideas of reference, and in the mentalizing ability (theory of mind [ToM]) of patients. Perceiving causal relationships may be understood either as higher order cognitive reasoning or as low-level information processing. In the present study, perception of causality was investigated as a low-level, preattentional capability similar to gestalt-like perceptual organization. Thirty-one patients (24 men and 7 women with mean age 27.7 years) and the same number of healthy control subjects matched to patients with respect to age and sex were tested. A visual paradigm was used in which 2 identical discs move, from opposite sides of a monitor, steadily toward and then past one another. Their coincidence generates an ambiguous, bistable percept (discs either "stream through" or "bounce off" one another). The bouncing perception, ie, perceived causality, is enhanced when auditory stimuli are presented at the time of coincidence. Psychopathology was measured using the Positive and Negative Syndrome Scale. It was found that positive symptoms were strongly associated with increased perceived causality and disorganization with attenuated perceived causality. Patients in general were not significantly different from controls, but symptom subgroups showed specifically altered perceived causality. Perceived causality as a basic preattentional process may contribute to higher order cognitive alterations and ToM deficiencies. It is suggested that cognitive remediation therapy should address both increased and reduced perception of causality.

In vitro drug causality assessment in Stevens-Johnson syndrome - alternatives for lymphocyte transformation test

BACKGROUND Patients with Stevens-Johnson syndrome (SJS) or toxic epidermal necrolysis (TEN) are often exposed simultaneously to a few potentially culprit drugs. However, both the standard lymphocyte transformation tests (LTT) with proliferation as the assay end-point as well as skin tests, if done, are often negative. OBJECTIVE As provocation tests are considered too dangerous, there is an urgent need to identify the relevant drug in SJS/TEN and to improve sensitivity of tests able to identify the causative drug. METHODS Fifteen patients with SJS/TEN with the ALDEN score ≥ 6 and 18 drug-exposed controls were included. Peripheral blood mononuclear cells (PBMC) were isolated and cultured under defined conditions with drugs. LTT was compared to the following end-points: cytokine levels in cell culture supernatant, number of granzyme B secreting cells by ELISpot and intracellular staining for granulysin and IFNγ in CD3(+) CD4(+), CD3(+) CD8(+) and NKp46(+) cells. To further enhance sensitivity, the effect of IL-7/IL-15 pre-incubation of PBMC was evaluated. RESULTS Lymphocyte transformation tests was positive in only 4/15 patients (sensitivity 27%, CI: 8-55%). Similarly, with granzyme B-ELISpot culprit drugs were positive in 5/15 patients (sensitivity 33%, CI: 12-62%). The expression of granulysin was significantly induced in NKp46(+) and CD3(+) CD4(+) cells (sensitivity 40%, CI: 16-68% and 53%, CI: 27-79% respectively). Cytokine production could be demonstrated in 38%, CI: 14-68% and 43%, CI: 18-71% of patients for IL-2 and IL-5, respectively, and in 55%, CI: 23-83% for IFNγ. Pre-incubation with IL-7/IL-15 enhanced drug-specific response only in a few patients. Specificities of tested assays were in the range of 95 (CI: 80-99%)-100% (CI: 90-100%). CONCLUSIONS AND CLINICAL RELEVANCE Granulysin expression in CD3(+) CD4(+) , Granzyme B-ELISpot and IFNγ production considered together provided a sensitivity of 80% (CI: 52-96%) and specificity of 95% (80-99%). Thus, this study demonstrated that combining different assays may be a feasible approach to identify the causative drug of SJS/TEN reactions; however, confirmation on another group of patients is necessary.

What do brain network interactions tell about cognitive processes? How to seek causality?

Optimal adjustment of brain networks allows the biased processing of information in response to the demand of environments and is therefore prerequisite for adaptive behaviour. It is widely shown that a biased state of networks is associated with a particular cognitive process. However, those associations were identified by backward categorization of trials and cannot provide a causal association with cognitive processes. This problem still remains a big obstacle to advance the state of our field in particular human cognitive neuroscience. In my talk, I will present two approaches to address the causal relationships between brain network interactions and behaviour. Firstly, we combined connectivity analysis of fMRI data and a machine leaning method to predict inter-individual differences of behaviour and responsiveness to environmental demands. The connectivity-based classification approach outperforms local activation-based classification analysis, suggesting that interactions in brain networks carry information of instantaneous cognitive processes. Secondly, we have recently established a brand new method combining transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and EEG. We use the method to measure signal transmission between brain areas while introducing extrinsic oscillatory brain activity and to study causal association between oscillatory activity and behaviour. We show that phase-matched oscillatory activity creates the phase-dependent modulation of signal transmission between brain areas, while phase-shifted oscillatory activity blunts the phase-dependent modulation. The results suggest that phase coherence between brain areas plays a cardinal role in signal transmission in the brain networks. In sum, I argue that causal approaches will provide more concreate backbones to cognitive neuroscience.

Causality Relationships between Total Exports with Agricultural and Manufacturing GDP in Tanzania

This paper, investigates causal relationships among agriculture, manufacturing and export in Tanzania by using time series data for the period between 1970 and 2005. The empirical results show in both sectors there is Granger causality where agriculture causes both exports and manufacturing. Exports also cause both agricultural GDP and manufacturing GDP and any two variables out of three jointly cause the third one. There is also some evidence that manufacturing does not cause export and agriculture. Regarding cointegration, pairwise agricultural GDP and export are cointegrated, export and manufacture are cointegrated. Agriculture and manufacture are cointegrated but they are lag sensitive. However, three variables, manufacturing, export and agriculture both together are cointegrated showing that they share long run relation and this has important economic implications.

Olfactory fMRI Connectivity Analysis Based on Granger Causality with Application in Anosmia Assessment

In this work, we describe hubs organization within the olfactory network with Functional Magnetic Resonance Imaging (fMRI). Granger causality analyses were applied in the supposed regions of interest (ROIs) involved in olfactory tasks, as described in [1]. We aim to get deeper knowledge about the hierarchy of the regions within the olfactory network and to describe which of these regions, in terms of strength of the connectivity, impair in different types of anosmia.

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Data analysis using circular causality in networks

Complex systems in causal relationships are known to be circular rather than linear; this means that a particular result is not produced by a single cause, but rather that both positive and negative feedback processes are involved. However, although interpreting systemic interrelationships requires a language formed by circles, this has only been developed at the diagram level, and not from an axiomatic point of view. The first difficulty encountered when analysing any complex system is that usually the only data available relate to the various variables, so the first objective was to transform these data into cause-and-effect relationships. Once this initial step was taken, our discrete chaos theory could be applied by finding the causal circles that will form part of the system attractor and allow their behavior to be interpreted. As an application of the technique presented, we analyzed the system associated with the transcription factors of inflammatory diseases.

A Sociocybernetics Data Analysis Using Causality in Tourism Networks

The aim of this paper is to propose a mathematical model to determine invariant sets, set covering, orbits and, in particular, attractors in the set of tourism variables. Analysis was carried out based on a pre-designed algorithm and applying our interpretation of chaos theory developed in the context of General Systems Theory. This article sets out the causal relationships associated with tourist flows in order to enable the formulation of appropriate strategies. Our results can be applied to numerous cases. For example, in the analysis of tourist flows, these findings can be used to determine whether the behaviour of certain groups affects that of other groups and to analyse tourist behaviour in terms of the most relevant variables. Unlike statistical analyses that merely provide information on current data, our method uses orbit analysis to forecast, if attractors are found, the behaviour of tourist variables in the immediate future.

Granger causality and systemic risk

Building on the concept of Granger causality in risk in Hong et al. (2009), and focusing on an international sample of large-capitalization banks, we test for predictability in comovements in the left tails of returns of individual banks and the global system. The main results show that large individual shocks (defined as balance-sheet contractions exceeding the 1% VaR level) are a strong predictor of subsequent shocks in the global system. This evidence is particularly strong for US banks with large desks of proprietary trading. Similarly, we document strong evidence of financial vulnerabilities (exposures) to systemic shocks in US subprime creditors.