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Incoherent speech in schizophrenia has long been described as the mind making “leaps” of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual “leaps” in spoken word meanings have used language-based embedding spaces. An embedding space represents meaning of words as numerical vectors where a greater proximity between word vectors represents more shared meaning. However, there are limitations with word vector-based operationalizations of coherence which can limit their appeal and utility in clinical practice. First, the use of esoteric word embeddings can be conceptually hard to grasp, and this is complicated by several different operationalizations of incoherent speech. This problem can be overcome by a better visualization of methods. Second, temporal information from the act of speaking has been largely neglected since models have been built using written text, yet speech is spoken in real time. This issue can be resolved by leveraging time stamped transcripts of speech. Third, contextual information - namely the situation of where something is spoken - has often only been inferred and never explicitly modeled. Addressing this situational issue opens up new possibilities for models with increased temporal resolution and contextual relevance. In this paper, direct visualizations of semantic distances are used to enable the inspection of examples of incoherent speech. Some common operationalizations of incoherence are illustrated, and suggestions are made for how temporal and spatial contextual information can be integrated in future implementations of measures of incoherence.

Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions that we want to assess. In this paper we reflect on two decades of experience with the application of language-based assessment to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it should be measured and why we are measuring the phenomena. We address the questions by advocating for a principled framework for aligning computational models to the constructs being assessed and the tasks being used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled approach can further the goal of transitioning language-based computational assessments to part of clinical practice while gaining the trust of critical stakeholders.

Cancer associated MSCs (CA-MSCs) are multipotent stromal cells that generate tumor desmoplasia and promote tumor growth. Here, we investigated the impact of CA-MSCs on the ovarian tumor immune microenvironment. In vitro and in vivo analyses confirmed that CA-MSCs secrete numerous cytokines and chemokines, including Ccl2, Cx3cl1 and Tgfb1, that are known to induce the recruitment of monocytes into the tumor and promote macrophage immunosuppressive phenotype. Using a syngeneic and immune responsive murine ovarian tumor model, we found that CA-MSCs restrict the intra-tumoral trafficking of CD8+ T cells and inhibit the response to immune checkpoint inhibitor therapy. Similarly, in patient samples the presence of CA-MSC is inversely correlated with CD8+ T cell infiltration into tumor islets. Single cell RNAseq analyses revealed that tumoral monocytes and macrophages of a-PD-L1 treated-CA-MSCs enriched tumors express high levels of the protein transforming growth factor beta induced (Tgfbi). In ovarian cancer patient samples TGFBI is primarily expressed in the desmoplastic stroma and correlated with poor prognosis. Importantly, we found that hedgehog signaling inhibitor (HHi) therapy reversed CA-MSC driven tumor desmoplasia, reduced the number of tumor-associated myeloid cells and decreased their expression of Tgfbi. These events restored the infiltration and activation of CD8+ T cells into the tumor islets and response to a-PDL1 therapy. We are currently evaluating the role of Tgfbi in the regulation of innate and adaptive immune response. Our findings demonstrated CA-MSCs critically regulate anti-tumor immunity and support HHi as an adjunct to anti-PDL1 therapy to increase repose to immune therapy in ovarian cancer.