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netts - NETworks of Transcript Semantics

Mapping a patient's speech as a network has proved to be a useful way of understanding formal thought disorder in psychosis. However, to date, graph theory tools have not incorporated the semantic content of speech, which is altered in psychosis. We developed an algorithm, netts, to map the semantic content of speech as a network, then applied netts to construct semantic speech networks for a general population sample, and a clinical sample comprising patients with first episode psychosis (FEP), people at clinical high risk of psychosis (CHR-P), and healthy controls. Semantic speech networks from the general population were more connected than size-matched randomised networks, with fewer and larger connected components, reflecting the non-random nature of speech. Networks from FEP patients were smaller than from healthy participants, for a picture description task but not a story recall task. For the former task, FEP networks were also more fragmented than those from controls; showing more, smaller connected components. CHR-P networks showed fragmentation values in-between FEP patients and controls. A clustering analysis suggested that semantic speech networks captured novel signal not already described by existing NLP measures. Network features were also related to negative symptom scores and scores on the Thought and Language Index, although these relationships did not survive correcting for multiple comparisons. Overall, these data suggest that semantic networks can enable deeper phenotyping of formal thought disorder in psychosis. We are releasing Netts as an open Python package alongside this manuscript.

Semantic speech networks linked to formal thought disorder in early psychosis

Mapping a patient's speech as a network has proved to be a useful way of understanding formal thought disorder in psychosis. However, to date, graph theory tools have not incorporated the semantic content of speech, which is altered in psychosis. We developed an algorithm, netts, to map the semantic content of speech as a network, then applied netts to construct semantic speech networks for a general population sample, and a clinical sample comprising patients with first episode psychosis (FEP), people at clinical high risk of psychosis (CHR-P), and healthy controls. Semantic speech networks from the general population were more connected than size-matched randomised networks, with fewer and larger connected components, reflecting the non-random nature of speech. Networks from FEP patients were smaller than from healthy participants, for a picture description task but not a story recall task. For the former task, FEP networks were also more fragmented than those from controls; showing more, smaller connected components. CHR-P networks showed fragmentation values in-between FEP patients and controls. A clustering analysis suggested that semantic speech networks captured novel signal not already described by existing NLP measures. Network features were also related to negative symptom scores and scores on the Thought and Language Index, although these relationships did not survive correcting for multiple comparisons. Overall, these data suggest that semantic networks can enable deeper phenotyping of formal thought disorder in psychosis. We are releasing Netts as an open Python package alongside this manuscript.

Cerebellar GABA change during visuomotor adaptation relates to adaptation performance and cerebellar network connectivity: A Magnetic Resonance Spectroscopic Imaging study.

Motor adaptation is crucial for performing accurate movements in a changing environment and relies on the cerebellum. Although cerebellar involvement has been well characterized, the neurochemical changes in the cerebellum that underpin human motor adaptation remain unknown. We used a novel Magnetic Resonance Spectroscopic Imaging (MRSI) technique to measure changes in the major inhibitory neurotransmitter γ-aminobutyric acid (GABA) in the human cerebellum during visuomotor adaptation. Participants used their right hand to adapt to a rotated cursor in the scanner, compared with a control task requiring no adaptation. We were able to spatially resolve adaptation-driven GABA changes at the cerebellar nuclei and in the cerebellar cortex in the left and the right cerebellar hemisphere independently and found that simple movement of the right hand increases GABA in the right cerebellar nuclei and decreases GABA in the left. When isolating adaptation-driven GABA changes, we found an increase in GABA in the left cerebellar nuclei and a decrease in GABA in the right cerebellar nuclei during adaptation. Early adaptation-driven GABA change in the right cerebellar nuclei correlated with adaptation performance: Participants showing greater GABA decrease adapted better, suggesting that this early GABA change is behaviourally relevant. Early GABA change also correlated with functional connectivity change in a cerebellar network: Participants showing a greater decrease in GABA also showed greater strength increase in cerebellar network connectivity. These results were specific to GABA, specific to adaptation and specific to the cerebellar network. This study provides the first evidence for plastic changes in cerebellar neurochemistry during a motor adaptation task. Characterising these naturally occurring neurochemical changes may provide a basis for developing therapeutic interventions to facilitate neurochemical changes in the cerebellum that can improve human motor adaptation.

Highlights from the 30th Annual Meeting of the Society for the Neural Control of Movement

GABA relates to functional connectivity changes and retention in visuomotor adaptation

Data supporting\" Learning to optimize perceptual decisions through suppressive interactions in the human brain\"

Learning to optimize perceptual decisions through suppressive interactions in the human brain