Deficits of entropy modulation in schizophrenia are predicted by functional connectivity strength in the theta band and structural clustering.

Spectral entropy (SE) allows comparing task-related modulation of electroencephalogram (EEG) between patients and controls, i.e. spectral changes of the EEG associated to task performance.

Functional EEG network analysis in schizophrenia: Evidence of larger segregation and deficit of modulation.

Objective: Higher mental functions depend on global cerebral functional coordination. Our aim was to study fast modulation of functional networks in schizophrenia that has not been previously assessed.

Methods: Graph-theory was used to analyze the electroencephalographic (EEG) activity during an odd-ball task in 57 schizophrenia patients (18 first episode patients, FEPs) and 59 healthy controls.

Modulation of brain network parameters associated with subclinical psychotic symptoms.

Objective: Static deficits in small-world properties of brain networks have been described in clinical psychosis, but task-related modulation of network properties has been scarcely studied. Our aim was to assess the modulation of those properties and its association with subclinical psychosis and cognition in the general population.

Method: Closeness centrality and small-worldness were compared between pre-stimulus baseline and response windows of an odd-ball task in 200 healthy individuals.

Decreased entropy modulation of EEG response to novelty and relevance in schizophrenia during a P300 task.

The analysis of the interaction between novelty and relevance may be of interest to test the aberrant salience hypothesis of schizophrenia (SCH). In comparison with other neuroimaging techniques, such as functional magnetic resonance imaging, electroencephalography (EEG) provides high temporal resolution. Therefore, EEG is useful to analyze transient dynamics in neural activity, even in the range of milliseconds.

Decreased spectral entropy modulation in patients with schizophrenia during a P300 task.

Spectral entropy (SE), also known as Shannon entropy, is a useful parameter for quantifying the global regularity of the electroencephalographic (EEG) signal. Hence, it is of interest in the assessment of the electrophysiological correlates of cognitive processing in schizophrenia. However, to date, SE has been barely used in studies comparing resting EEG recordings between patients and controls.