Many published studies report that antipsychotic therapy is associated with thrombotic tendency. However, fewer studies have examined whether thrombotic tendency exists in unmedicated schizophrenia (SZ) or other psychoses. In addition, some studies have reported a full remission of psychotic symptoms with warfarin, a well-known anticoagulant, raising the possibility that psychosis may be associated with thrombotic tendency. Here, we summarize the available literature on biomarkers of thrombotic tendency in unmedicated patients with SZ and other psychoses.
A PubMed search using the keywords “psychosis” OR “schizophrenia” AND (“coagulation” OR “tissue plasminogen activator” OR “thromboembolism”) for studies published between 2012 and 2023 yielded 290 results. Inclusion criteria were 1) controlled studies, 2) studies including patients with psychosis, 3) English language. Exclusion criteria included 1) review articles, 2) case reports, 3) focus on antipsychotics as a factor in thrombotic tendency.
Seven studies met criteria and were included for qualitative synthesis in this review. Five studies included patients with SZ and related psychoses, while two studies also included patients with major depression and bipolar disorders. Numerous plasma proteins involved in regulating coagulation were identified as being low in patients with SZ, including fibrinolytic enzymes such as tissue-type plasminogen activator (tPA), plasmin, protein S, and plasminogen, although one study found that tPA was reduced in chronic SZ but elevated in first-episode SZ (FES) patients.
Those reports suggestive of thrombotic tendency in schizophrenia and related psychoses warrant further investigation, especially in drug-naïve first episode samples as compared to chronic patients, where antipsychotic treatment may contribute to thrombotic tendency. If confirmed, therapeutic strategies with anticoagulants like tPA may represent a novel approach to managing schizophrenia.
The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches.
We investigated affective processing in aphantasia (= absent or reduced vividness of mental imagery), considering a possible overlap with alexithymia (= deficits in identifying and describing emotions), as reduced vividness of mental imagery is also reported in alexithymia. Study 1 assessed physiological reactions and self-reported sympathy in n = 30 individuals with aphantasia and n = 75 controls when confronted to visual and verbal material showing people in distress. Results demonstrated that individuals with aphantasia show reduced emotional responses, especially to verbal stimuli. This is of particular importance given the higher prevalence of alexithymic symptoms in aphantasic participants, notably in externally-oriented thinking and difficulties in describing feelings. An additional mediation analysis confirmed that vividness of visual imagery mediated the association between alexithymia and self-reported sympathy. Study 2 extended our exploration to the recognition of emotions in others using the same sample. Despite accurate recognition of emotions, individuals with aphantasia exhibited significantly slower response times, suggesting less efficient strategies that do not involve mental imagery. Our findings highlight the crucial role of mental imagery in the interplay of cognitive functions and affective processes, demonstrating how conditions such as aphantasia and alexithymia can affect sympathy and, more generally, emotions.
Smartphone-based digital phenotyping can provide novel transdiagnostic markers of mental illness including circadian routines and anhedonia. In proposing transdiagnostic digital phenotypes for circadian routines and anhedonia in depression and bipolar disorder patients, this paper explores their derivation, comparison to naive models, and replicability across two different research sites/teams.
84 participants (bipolar disorder, depression, controls) used the mindLAMP app for 12 weeks to capture digital phenotypes on their personal smartphones. mindLAMP was used to deliver surveys about mood symptoms while collecting device acceleration, geolocation, and screen on/off state. Participant chronotype was derived from this sensor data. Within-participant and between-participant models were created to assess how time-varying features collected through digital phenotyping could predict weekly anhedonia survey responses.
Within-person models outperformed between-person models in predicting anhedonia. Chronotype was the strongest predictor of weekly anhedonia scores as indicated by Shapley scores. Shapley scores also revealed that many of the time-varying predictor variables are significant but differ in their direction of action.
This analysis reveals the meaningful but potentially misleading nature of digital phenotyping signals. Results suggest that each participant has a unique set of relationships between time-varying digital phenotype variables; therefore, it is challenging to predict trends between participants. Bayesian models, with appropriate population priors, may offer the next step for improving the potential of personalized digital phenotyping insights.

