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Attempts to identify causal interactions in multivariable biological time series (e.g., gene data, protein data, physiological data) can be undermined by the confounding influence of environmental (exogenous) inputs. Compounding this problem, we are commonly only able to record a subset of all related variables in a system. These recorded variables are(More)
Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in(More)
There is still no clear consensus as to which of the many functional and structural changes in the brain in schizophrenia are of most importance, although the main focus to date has been on those in the frontal and cingulate cortices. In the present study, we have used a novel holistic approach to identify brain-wide functional connectivity changes in(More)
BACKGROUND The efficiency of human brain depends on the integrity of both long- and short-range connections, but the long-range connections need to be "penalized" to reduce overall wiring costs. This principle, termed as the anatomical distance function (ADF), refers to the presence of an inverse relationship between anatomical distance and connectivity. A(More)
Schizophrenia is associated with disconnectivity in the brain although it is still unclear whether changes within or between hemispheres are of greatest importance. In this paper, an analysis of 152 schizophrenia patients compared with 122 healthy controls was carried out. Comparisons were also made with 39 depression patients and 37 controls to examine(More)
BACKGROUND Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or(More)
BACKGROUND We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes. METHODOLOGY Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation(More)
BACKGROUND Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are(More)
BACKGROUND Previous studies imply that interhemispheric disconnectivity plays a more important role on information processing in schizophrenia. However, the role of the aberrant interhemispheric connection in the pathophysiology of this disorder remains unclear. Recently, resting-state functional Magnetic Resonance Imaging (fMRI) has reported to have(More)
A question of great interest in systems biology is how to uncover complex network structures from experimental data[1, 3, 18, 38, 55]. With the rapid progress of experimental techniques, a crucial task is to develop methodologies that are both statistically sound and computationally feasible for analysing increasingly large datasets and reliably inferring(More)