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Abstract Redox, a native modality in biology involving the flow of electrons, energy, and information, is used for energy‐harvesting, biosynthesis, immune‐defense, and signaling. Because electrons (in contrast to protons) are not soluble in the medium, electron‐flow through the redox modality occurs through redox reactions that are sometimes organized into pathways and networks (e.g., redox interactomes). Redox is also accessible to electrochemistry, which enables electrodes to receive and transmit electrons to exchange energy and information with biology. In this Perspective, efforts to develop electrochemistry as a tool for redox‐based bio‐information processing: to interconvert redox‐based molecular attributes into interpretable electronic signals, are described. Using a series of Case Studies, how the information‐content of the measurements can be enriched using: diffusible mediators; tuned electrical input sequences; and cross‐modal measurements (e.g., electrical plus spectral), is shown. Also, theory‐guided feature engineering approaches to compress the information in the electronic signals into quantitative metrics (i.e., features) that can serve as correlating variables for pattern recognition by data‐driven analysis are described. Finally, how redox provides a modality for electrogenetic actuation is illustrated. It is suggested that electrochemistry's capabilities to provide real‐time, low‐cost, and high‐content data in an electronic format allow the feedback‐control needed for autonomous learning and deployable sensing/actuation. 

Abstract Although digital health solutions are increasingly popular in clinical psychiatry, one application that has not been fully explored is the utilization of survey technology to monitor patients outside of the clinic. Supplementing routine care with digital information collected in the “clinical whitespace” between visits could improve care for patients with severe mental illness. This study evaluated the feasibility and validity of using online self-report questionnaires to supplement in-person clinical evaluations in persons with and without psychiatric diagnoses. We performed a rigorous in-person clinical diagnostic and assessment battery in 54 participants with schizophrenia (N = 23), depressive disorder (N = 14), and healthy controls (N = 17) using standard assessments for depressive and psychotic symptomatology. Participants were then asked to complete brief online assessments of depressive (Quick Inventory of Depressive Symptomatology) and psychotic (Community Assessment of Psychic Experiences) symptoms outside of the clinic for comparison with the ground-truth in-person assessments. We found that online self-report ratings of severity were significantly correlated with the clinical assessments for depression (two assessments used: R = 0.63, p < 0.001; R = 0.73, p < 0.001) and psychosis (R = 0.62, p < 0.001). Our results demonstrate the feasibility and validity of collecting psychiatric symptom ratings through online surveys. Surveillance of this kind may be especially useful in detecting acute mental health crises between patient visits and can generally contribute to more comprehensive psychiatric treatment. 

This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e.g. hallucinations and delusions), using two distinct channel-delay correlation methods. We show that the schizophrenic subjects with strong positive symptoms and who are markedly ill pose complex articulatory coordination pattern in facial and speech gestures than what is observed in healthy subjects. This distinction in speech coordination pattern is used to train a multimodal convolutional neural network (CNN) which uses video and audio data during speech to distinguish schizophrenic patients with strong positive symptoms from healthy subjects. We also show that the vocal tract variables (TVs) which correspond to place of articulation and glottal source outperform the Mel-frequency Cepstral Coefficients (MFCCs) when fused with Facial Action Units (FAUs) in the proposed multimodal network. For the clinical dataset we collected, our best performing multimodal network improves the mean F1 score for detecting schizophrenia by around 18% with respect to the full vocal tract coordination (FVTC) baseline method implemented with fusing FAUs and MFCCs.