[Comp-neuro] New Paper: about Speech Graphs and Psychosis
napvasconcelos at gmail.com
Tue Apr 10 03:08:49 CEST 2012
Mota NB , Vasconcelos NAP , Lemos N , Pieretti AC , Kinouchi O , et al.
(2012) *Speech Graphs Provide a Quantitative Measure of Thought Disorder in
*.PLoS ONE 7(4): e34928. doi:10.1371/journal.pone.0034928
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.
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.
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.
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