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Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed(More)
We describe a Bayesian filtering scheme for nonlinear state-space models in continuous time. This scheme is called Generalised Filtering and furnishes posterior conditional densities on hidden states and unknown parameters generating observed data. Crucially, the scheme operates online, assimilating data to optimize the conditional density on time-varying(More)
BACKGROUND Previous studies have suggested that the default mode network (DMN) plays a central role in the physiopathology of major depressive disorder (MDD). However, the effect of antidepressant treatment on functional connectivity within the DMN has yet to be established. Considering the very high rates of relapse in recovered subjects, we hypothesized(More)
This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure(More)
This paper is about the fitting or inversion of dynamic causal models (DCMs) of fMRI time series. It tries to establish the validity of stochastic DCMs that accommodate random fluctuations in hidden neuronal and physiological states. We compare and contrast deterministic and stochastic DCMs, which do and do not ignore random fluctuations or noise on hidden(More)
The default mode network (DMN) has recently attracted widespread interest. Previous studies have found that task-related processing can induce deactivation and changes in the functional connectivity of this network. However, it remains unclear how tasks modulate the underlying effective connectivity within the DMN. Using recent advances in dynamic causal(More)
Understanding the neural basis of poor impulse control in Internet addiction (IA) is important for understanding the neurobiological mechanisms of this syndrome. The current study investigated how neuronal pathways implicated in response inhibition were affected in IA using a Go-Stop paradigm and functional magnetic resonance imaging (fMRI). Twenty-three(More)
An enhanced understanding of how normal aging alters brain structure is urgently needed for the early diagnosis and treatment of age-related mental diseases. Structural magnetic resonance imaging (MRI) is a reliable technique used to detect age-related changes in the human brain. Currently, multivariate pattern analysis (MVPA) enables the exploration of(More)
BACKGROUND Numerous studies have demonstrated the higher-order functions of the cerebellum, including emotion regulation and cognitive processing, and have indicated that the cerebellum should therefore be included in the pathophysiological models of major depressive disorder. The aim of this study was to compare the resting-state functional connectivity of(More)
BACKGROUND Previous neuroimaging studies have documented changes in the brain of heroin addicts. However, few researches have detailed whether such changes can be amended after short-term abstinence. METHODS We used magnetic resonance imaging (MRI) to investigate gray matter volume in 20 heroin-dependent patients at 3 days and at 1 month after heroin(More)