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1. Evaluating the Use of Online Self-Report Questionnaires as Clinically Valid Mental Health Monitoring Tools in the Clinical Whitespace

   https://doi.org/10.1007/s11126-023-10022-1  

   Arrow, Kaitlyn ; Resnik, Philip ; Michel, Hanna ; Kitchen, Christopher ; Mo, Chen ; Chen, Shuo ; Espy-Wilson, Carol ; Coppersmith, Glen ; Frazier, Colin ; Kelly, Deanna L. ( May 2023 , Psychiatric Quarterly)

   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 healthmore »crises between patient visits and can generally contribute to more comprehensive psychiatric treatment.

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2. Binge‐eating disorder with and without lifetime anorexia nervosa: A comparison of sociodemographic and clinical features

   https://doi.org/10.1002/eat.23858  

   Pawar, Pratiksha S. ; Thornton, Laura M. ; Flatt, Rachael E. ; Sanzari, Christina M. ; Carrino, Emily A. ; Tregarthen, Jenna P. ; Argue, Stuart ; Bulik, Cynthia M. ; Watson, Hunna J. ( November 2022 , International Journal of Eating Disorders)

   To compare individuals who have experienced binge‐eating disorder (BED) and anorexia nervosa (AN) (BED AN+) to those who have experienced BED and not AN (BED AN–).

   Participants (N = 898) met criteria for lifetime BED and reported current binge eating. Approximately 14% had a lifetime diagnosis of AN. Analyses compared BED AN+ and BED AN– on sociodemographic variables and clinical history.

   The presence of lifetimeANwas associated with more severe eating disorder symptoms, including earlier onset, more frequent, more chronic, and more types of eating disorder behaviors over the lifetime, as well as a higher lifetime prevalence of bulimia nervosa (BN). Participants with lifetimeANreported being more likely to have received treatments forBEDorBN, had significantly lower minimum, current, and maximumBMIs, had more severe general anxiety, and were significantly more likely to be younger and female. In the full sample, the lifetime prevalence of unhealthy weight control behaviors was high and treatment utilization was low, despite an average 15‐year history since symptom onset. Gastrointestinal disorders and comorbid anxiety, depression, and attention‐deficit/hyperactivity disorder symptoms were prevalent.

   Individuals fared poorly on a wide array of domains, yet those with lifetimeANfared considerably more poorly. All patients withBEDshould be screenedmore »for mental health and gastrointestinal comorbidities and offered referral and treatment options.

   Individuals experiencing binge‐eating disorder have severe symptomology, but those who have experienced binge‐eating disorder and anorexia nervosa fare even more poorly. Our study emphasizes that patients with binge‐eating disorder would benefit from being screened for mental health and gastrointestinal comorbidities, and clinicians should consider history of unhealthy weight control behaviors to inform treatment and relapse prevention.

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3. Identifying Mood Episodes Using Dialogue Features from Clinical Interviews

   https://doi.org/10.21437/interspeech.2019-1878  

   Aldeneh, Zakaria ; Jaiswal, Mimansa ; Picheny, Michael ; McInnis, Melvin G. ; Provost, Emily Mower ( September 2019 , Interspeech)

   Bipolar disorder, a severe chronic mental illness characterized by pathological mood swings from depression to mania, requires ongoing symptom severity tracking to both guide and measure treatments that are critical for maintaining long-term health. Mental health professionals assess symptom severity through semi-structured clinical interviews. During these interviews, they observe their patients’ spoken behaviors, including both what the patients say and how they say it. In this work, we move beyond acoustic and lexical information, investigating how higher-level interactive patterns also change during mood episodes. We then perform a secondary analysis, asking if these interactive patterns, measured through dialogue features, can be used in conjunction with acoustic features to automatically recognize mood episodes. Our results show that it is beneficial to consider dialogue features when analyzing and building automated systems for predicting and monitoring mood.
4. Deep Learning in Neuroimaging: Overcoming Challenges With Emerging Approaches

   https://doi.org/10.3389/fpsyt.2022.912600  

   Smucny, Jason ; Shi, Ge ; Davidson, Ian ( June 2022 , Frontiers in Psychiatry)

   Deep learning (DL) is of great interest in psychiatry due its potential yet largely untapped ability to utilize multidimensional datasets (such as fMRI data) to predict clinical outcomes. Typical DL methods, however, have strong assumptions, such as large datasets and underlying model opaqueness, that are suitable for natural image prediction problems but not medical imaging. Here we describe three relatively novel DL approaches that may help accelerate its incorporation into mainstream psychiatry research and ultimately bring it into the clinic as a prognostic tool. We first introduce two methods that can reduce the amount of training data required to develop accurate models. These may prove invaluable for fMRI-based DL given the time and monetary expense required to acquire neuroimaging data. These methods are (1) transfer learning − the ability of deep learners to incorporate knowledge learned from one data source (e.g., fMRI data from one site) and apply it toward learning from a second data source (e.g., data from another site), and (2) data augmentation (via Mixup) − a self-supervised learning technique in which “virtual” instances are created. We then discuss explainable artificial intelligence (XAI), i.e., tools that reveal what features (and in what combinations) deep learners use to makemore »decisions. XAI can be used to solve the “black box” criticism common in DL and reveal mechanisms that ultimately produce clinical outcomes. We expect these techniques to greatly enhance the applicability of DL in psychiatric research and help reveal novel mechanisms and potential pathways for therapeutic intervention in mental illness.« less
5. Design of a Neurocognitive Digital Health System (NDHS) for Neurodegenerative Diseases

   https://doi.org/10.1145/3469266.3471157  

   Templeton, John M. ; Poellabauer, Christian ; Schneider, Sandra ( June 2021 , Workshop on Future of Digital Biomarkers)

   Digital health technology is becoming more ubiquitous in monitoring individuals’ health as both device functionality and overall prevalence increase. However, as individuals age, challenges arise with using this technology particularly when it involves neurodegenerative issues (e.g., for individuals with Parkinson’s disease, Alzheimer’s disease, and ALS). Traditionally, neurodegenerative diseases have been assessed in clinical settings using pen-and-paper style assessments; however, digital health systems allow for the collection of far more data than we ever could achieve using traditional methods. The objective of this work is the formation and implementation of a neurocognitive digital health system designed to go beyond what pen-and-paper based solutions can do through the collection of (a) objective, (b) longitudinal, and (c) symptom-specific data, for use in (d) personalized intervention protocols. This system supports the monitoring of all neurocognitive functions (e.g., motor, memory, speech, executive function, sensory, language, behavioral and psychological function, sleep, and autonomic function), while also providing methodologies for personalized intervention protocols. The use of specifically designed tablet-based assessments and wearable devices allows for the collection of objective digital biomarkers that aid in accurate diagnosis and longitudinal monitoring, while patient reported outcomes (e.g., by the diagnosed individual and caregivers) give additional insights for use in themore »formation of personalized interventions. As many interventions are a one-size-fits-all concept, digital health systems should be used to provide a far more comprehensive understanding of neurodegenerative conditions, to objectively evaluate patients, and form personalized intervention protocols to create a higher quality of life for individuals diagnosed with neurodegenerative diseases.« less