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. 2023 Mar 22;49(Suppl 2):S142–S152. doi: 10.1093/schbul/sbac056

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© The Author(s) 2023. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Fig. 5. — Clustered speech measures. Semantic speech network measures captured signal complementary to other NLP measures. Shown is a heatmap of Pearson’s correlations between semantic speech network measures and NLP measures in the clinical dataset. Black lines indicate communities detected using the Louvain method. The measures used in this analysis were the novel netts measures, as well as basic transcript measures and established NLP measures. Netts measures were number of connected components (CC Number), mean connected component size (CC Mean Size), and median connected component size (CC Median Size). Basic transcript measures were number of words, number of sentences, and mean sentence length. Established NLP measures included Tangentiality, Ambiguous Pronouns, Semantic Coherence (Coherence), On-Topic Score (On Topic) taken from Morgan et al.²² Additionally, syntactic network measures based on the method proposed by Mota et al,^11,12 were taken from Morgan et al²² and included number of nodes in the largest strongly connected component of syntactic networks (LSC), number of nodes in the largest weakly connected component of syntactic networks (LCC), as well as the LSC and LCC normalized to random networks (LSCr, LCCr).¹² Pearson's correlations were calculated from each subject's average NLP value. Values were obtained for each measure by averaging across values calculated from the eight TAT picture descriptions.