Speech Graphs Provide a Quantitative Measure of Thought Disorder in Psychosis

Background: Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. Methodology/Principal Findings: To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. Conclusions/Significance: The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.FINEP [01.06.1092.00]FINEPCNPq Universal [481506/2007-1]CNPq UniversalCNPqCNPqCapesCAPESad Associacao Alberto Santos Dumont para Apoio a Pesquisa (AASDAP)a'd Associacao Alberto Santos Dumont para Apoio a Pesquisa (AASDAP

Persistent Hyperdopaminergia Decreases the Peak Frequency of Hippocampal Theta Oscillations during Quiet Waking and REM Sleep

Long-term changes in dopaminergic signaling are thought to underlie the pathophysiology of a number of psychiatric disorders. Several conditions are associated with cognitive deficits such as disturbances in attention processes and learning and memory, suggesting that persistent changes in dopaminergic signaling may alter neural mechanisms underlying these processes. Dopamine transporter knockout (DAT-KO) mice exhibit a persistent five-fold increase in extracellular dopamine levels. Here, we demonstrate that DAT-KO mice display lower hippocampal theta oscillation frequencies during baseline periods of waking and rapid-eye movement sleep. These altered theta oscillations are not reversed via treatment with the antidopaminergic agent haloperidol. Thus, we propose that persistent hyperdopaminergia, together with secondary alterations in other neuromodulatory systems, results in lower frequency activity in neural systems responsible for various cognitive processes

Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

In 1949, Donald Hebb postulated that assemblies of synchronously activated neurons are the elementary units of information processing in the brain. Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two decades due to technological advances. However, while the technology for the simultaneous recording of large neuronal populations undergoes fast development, there is still a paucity of analytical methods that can properly detect and track the activity of cell assemblies. Here we describe a principal component-based method that is able to (1) identify all cell assemblies present in the neuronal population investigated, (2) determine the number of neurons involved in ensemble activity, (3) specify the precise identity of the neurons pertaining to each cell assembly, and (4) unravel the time course of the individual activity of multiple assemblies. Application of the method to multielectrode recordings of awake and behaving rats revealed that assemblies detected in the cerebral cortex and hippocampus typically contain overlapping neurons. The results indicate that the PCA method presented here is able to properly detect, track and specify neuronal assemblies, irrespective of overlapping membership