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Background: Electroretinographic dysfunction is observed in psychosis, with a-wave and b-wave amplitudes potentially serving as biomarkers. Insulin resistance (IR) and childhood trauma (CT) have also been associated with psychosis-spectrum disorders, particularly schizophrenia. Electroretinographic dysfunction in early-course psychosis (EP) lacks exploration. This case-control exploratory study aimed to understand electroretinographic dysfunction in EP and its relationship with IR and CT. Methods: The study involved healthy controls (n=13) and EP individuals (n=14) and included photopic and scotopic flash-electroretinography (fERG), blood collection for IR assessment, and the Childhood Trauma Questionnaire (CTQ). Data were analyzed using SPSS v.29.0. Case-control differences across fERG conditions were explored using repeated-measures ANCOVA (3 flash conditions X 2 groups) adjusted for gender and age. Sub-analyses included Fisher’s, Mann-Whitney, partial correlations, and logistic and linear regressions. Results: Compared to controls, EP participants showed (1) lower photopic a-wave and b-wave amplitudes, specifically in the left eye and under the P1 condition, (2) greater odds for IR, and (3) higher CTQ scores. IR was associated with a higher CTQ score and a lower P2b amplitude. A higher CTQ score was associated with lower P2b amplitude in the left eye when adjusting for its interacting effect with IR. Conclusion: These findings suggest lower photopic a-wave and b-wave amplitudes, IR, and CT are explanatory markers in EP. CT may dysregulate the hypothalamic-pituitary-adrenal axis, increasing the risk of experiencing later-life psychosis. IR might be a biomarker in psychosis, contributing to electroretinographic dysfunction and neurodegeneration of cone post-synaptic cells. Further investigations are needed.

Background The proposed research aims to test the effects and mechanisms of a six-month yoga-based intervention as an add-on to standard treatment in opioid use disorder (OUD) by conducting a randomized controlled study with the following primary outcome variables: 1) clinical: abstinence (opioid negative urine test), and reductions in pain and craving, and 2) mechanisms: reward circuit activation in response to opioid visual cue craving paradigm, activation in response to a cognitive control task, and resting state functional connectivity through fMRI, and plasma beta-endorphin levels. Secondary outcome variables are perceived stress, anxiety, sleep quality, cognitive performance, pain threshold, buprenorphine dosage and side effects, withdrawal symptoms, socio-occupational functioning, vedic personality traits, heart rate variability, serum cortisol, and brain GABA levels through magnetic resonance spectroscopy (MRS). Methods In this single-blinded, randomized, controlled, parallel-group superiority trial with 1:1 allocation ratio, 164 patients with OUD availing the outpatient/ inpatient clinical services at a tertiary mental healthcare hospital in India will be enrolled after giving informed consent. Consecutive consenting patients will be randomly allotted to one of the two groups – yoga arm (standard treatment + yoga-based intervention), or waitlist group (standard treatment alone). Allocation concealment will be followed, the clinicians, outcome assessors and data analysts will remain blind to subject-group allocation. A validated and standardized yoga program for OUD will be used as an intervention. Participants in the yoga arm will receive 10 supervised in-person sessions of yoga in the initial two weeks followed by tele-yoga sessions thrice a week for the next 22 weeks. The wait-list control group will continue the standard treatment alone for 24 weeks. Assessments will be done at baseline, two weeks, 12 weeks, and 24 weeks. Data from all randomized subjects will be analysed using intent-to-treat analysis and mixed model multivariate analysis. Dissemination Findings will be disseminated through peer-reviewed publication, conference presentations, and social media. Trial registration number The trial has been registered under Clinical Trials Registry-India with registration number CTRI/2023/03/050737.

Abstract Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal “attacks” (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.

BackgroundCoordinated specialty care (CSC) has demonstrated efficacy in improving outcomes in individuals at clinical high risk for psychosis and individuals with first-episode psychosis. Given the limitations of scalability and staffing needs, the augmentation of services using digital mental health interventions (DMHIs) may be explored to help support CSC service delivery. ObjectiveIn this study, we aimed to understand the methods to implement and support technology in routine CSC and offered insights from a quality improvement study assessing the implementation outcomes of DMHIs in CSC. MethodsPatients and clinicians including psychiatrists, therapists, and supported education and employment specialists from a clinical-high-risk-for-psychosis clinic (Center for Early Detection Assessment and Response to Risk [CEDAR]) and a first-episode–psychosis clinic (Advancing Services for Psychosis Integration and Recovery [ASPIRE]) participated in a quality improvement project exploring the feasibility of DMHIs following the Access, Alignment, Connection, Care, and Scalability framework to implement mindLAMP, a flexible and evidenced-based DMHI. Digital navigators were used at each site to assist clinicians and patients in implementing mindLAMP. To explore the differences in implementation outcomes associated with the app format, a menu-style format was delivered at CEDAR, and a modular approach was used at ASPIRE. Qualitative baseline and follow-up data were collected to assess the specific implementation outcomes. ResultsIn total, 5 patients (ASPIRE: n=3, 60%; CEDAR: n=2, 40%) were included: 3 (60%) White individuals, 2 (40%) male and 2 (40%) female patients, and 1 (20%) transgender man, with a mean age of 19.6 (SD 2.05) years. Implementation outcome data revealed that patients and clinicians demonstrated high accessibility, acceptability, interest, and belief in the sustainability of DMHIs. Clinicians and patients presented a wide range of interest in unique use cases of DMHI in CSC and expressed variable feasibility and appropriateness associated with nuanced barriers and needs. In addition, the results suggest that adoption, penetration, feasibility, and appropriateness outcomes were moderate and might continue to be explored and targeted. ConclusionsImplementation outcomes from this project suggest the need for a patient- and clinician-centered approach that is guided by digital navigators and provides versatility, autonomy, and structure. Leveraging these insights has the potential to build on growing research regarding the need for versatility, autonomy, digital navigator support, and structured applications. We anticipate that by continuing to research and improve implementation barriers impeding the adoption and penetration of DMHIs in CSC, accessibility and uptake of DMHIs will improve, therefore connecting patients to the demonstrated benefits of technology-augmented care.

Convolutional neural networks (CNN) have demonstrated good accuracy and speed in spatially registering high signal-to-noise ratio (SNR) structural magnetic resonance imaging (sMRI) images. However, some functional magnetic resonance imaging (fMRI) images, e.g., those acquired from arterial spin labeling (ASL) perfusion fMRI, are of intrinsically low SNR and therefore the quality of registering ASL images using CNN is not clear. In this work, we aimed to explore the feasibility of a CNN-based affine registration network (ARN) for registration of low-SNR three-dimensional ASL perfusion image time series and compare its performance with that from the state-of-the-art statistical parametric mapping (SPM) algorithm. The six affine parameters were learned from the ARN using both simulated motion and real acquisitions from ASL perfusion fMRI data and the registered images were generated by applying the transformation derived from the affine parameters. The speed and registration accuracy were compared between ARN and SPM. Several independent datasets, including meditation study (10 subjects × 2), bipolar disorder study (26 controls, 19 bipolar disorder subjects), and aging study (27 young subjects, 33 older subjects), were used to validate the generality of the trained ARN model. The ARN method achieves superior image affine registration accuracy (total translation/total rotation errors of ARN vs. SPM: 1.17 mm/1.23° vs. 6.09 mm/12.90° for simulated images and reduced MSE/L1/DSSIM/Total errors of 18.07% / 19.02% / 0.04% / 29.59% for real ASL test images) and 4.4 times (ARN vs. SPM: 0.50 s vs. 2.21 s) faster speed compared to SPM. The trained ARN can be generalized to align ASL perfusion image time series acquired with different scanners, and from different image resolutions, and from healthy or diseased populations. The results demonstrated that our ARN markedly outperforms the iteration-based SPM both for simulated motion and real acquisitions in terms of registration accuracy, speed, and generalization.

IntroductionPersistent negative symptoms (PNS) are described as continuing moderate negative symptoms. More severe negative symptoms have been associated with poor premorbid functioning in both chronic schizophrenia and first episode psychosis patients. Furthermore, youth at clinical high risk (CHR) for developing psychosis may also present with negative symptoms and poor premorbid functioning. The aim of this current study was to: (1) define the relationship between PNS and premorbid functioning, life events, trauma and bullying, previous cannabis use, and resource utilization, and (2) to examine what explanatory variables best predicted PNS.MethodCHR participants (N = 709) were recruited from the North American Prodrome Longitudinal Study (NAPLS 2). Participants were divided into two groups: those with PNS (n = 67) versus those without PNS (n = 673). A K-means cluster analysis was conducted to distinguish patterns of premorbid functioning across the different developmental stages. The relationships between premorbid adjustment and other variables were examined using independent samples t-tests or chi square for categorical variables.ResultsThere was significantly more males in the PNS group. Participants with PNS had significantly lower levels of premorbid adjustment in childhood, early adolescence, and late adolescence, compared to CHR participants without PNS. There were no differences between the groups in terms of trauma, bullying, and resource utilization. The non-PNS group had more cannabis use and more desirable and non-desirable life events.ConclusionIn terms of better understanding relationships between early factors and PNS, a prominent factor associated with PNS was premorbid functioning, in particular poor premorbid functioning in later adolescence.

Brain aging, which involves a progressive loss of neuronal functions, has been reported to be premature in probands affected by schizophrenia (SCZ). Evidence shows that SCZ and accelerated aging are linked to changes in epigenetic clocks. Recent cross-sectional magnetic resonance imaging analyses have uncovered reduced brain reserves and connectivity in patients with SCZ compared to typically aging individuals. These data may indicate early abnormalities of neuronal function following cyto-architectural alterations in SCZ. The current mechanistic knowledge on brain aging, epigenetic changes, and their neuropsychiatric disease association remains incomplete. With this review, we explore and summarize evidence that the dynamics of gut-resident bacteria can modulate molecular brain function and contribute to age-related neurodegenerative disorders. It is known that environmental factors such as mode of birth, dietary habits, stress, pollution, and infections can modulate the microbiota system to regulate intrinsic neuronal activity and brain reserves through the vagus nerve and enteric nervous system. Microbiota-derived molecules can trigger continuous activation of the microglial sensome, groups of receptors and proteins that permit microglia to remodel the brain neurochemistry based on complex environmental activities. This remodeling causes aberrant brain plasticity as early as fetal developmental stages, and after the onset of first-episode psychosis. In the central nervous system, microglia, the resident immune surveillance cells, are involved in neurogenesis, phagocytosis of synapses and neurological dysfunction. Here, we review recent emerging experimental and clinical evidence regarding the gut-brain microglia axis involvement in SCZ pathology and etiology, the hypothesis of brain reserve and accelerated aging induced by dietary habits, stress, pollution, infections, and other factors. We also include in our review the possibilities and consequences of gut dysbiosis activities on microglial function and dysfunction, together with the effects of antipsychotics on the gut microbiome: therapeutic and adverse effects, role of fecal microbiota transplant and psychobiotics on microglial sensomes, brain reserves and SCZ-derived accelerated aging. We end the review with suggestions that may be applicable to the clinical setting. For example, we propose that psychobiotics might contribute to antipsychotic-induced therapeutic benefits or adverse effects, as well as reduce the aging process through the gut-brain microglia axis. Overall, we hope that this review will help increase the understanding of SCZ pathogenesis as related to chronobiology and the gut microbiome, as well as reveal new concepts that will serve as novel treatment targets for SCZ.

BackgroundElevated rates of alcohol, tobacco, and cannabis use are observed in both patients with psychotic disorders and individuals at clinical high risk for psychosis (CHR-P), and strong genetic associations exist between substance use disorders and schizophrenia. While individuals with 22q11.2 deletion syndrome (22qDel) are at increased genetic risk for psychosis, initial evidence suggests that they have strikingly low rates of substance use. In the current study, we aimed to directly compare substance use patterns and their neurobehavioral correlates in genetic and clinical high-risk cohorts.MethodsData on substance use frequency and severity, clinical symptoms, and neurobehavioral measures were collected at baseline and at 12-month follow-up visits in two prospective longitudinal cohorts: participants included 89 22qDel carriers and 65 age and sex-matched typically developing (TD) controls (40.67% male, Mage = 19.26 ± 7.84 years) and 1,288 CHR-P youth and 371 matched TD controls from the North American Prodrome Longitudinal Study-2 and 3 (55.74% male; Mage = 18.71 ± 4.27 years). Data were analyzed both cross-sectionally and longitudinally using linear mixed effects models.ResultsControlling for age, sex, and site, CHR-P individuals had significantly elevated rates of tobacco, alcohol, and cannabis use relative to TD controls, whereas 22qDel had significantly lower rates. Increased substance use in CHR-P individuals was associated with increased psychosis symptom severity, dysphoric mood, social functioning, and IQ, while higher social anhedonia was associated with lower substance use across all domains at baseline. These patterns persisted when we investigated these relationships longitudinally over one-year. CHR-P youth exhibited significantly increased positive psychosis symptoms, dysphoric mood, social functioning, social anhedonia, and IQ compared to 22qDel carriers, and lower rates of autism spectrum disorder (ASD) compared to 22qDel carriers, both at baseline and at 1 year follow-up.ConclusionIndividuals at genetic and CHR-P have strikingly different patterns of substance use. Factors such as increased neurodevelopmental symptoms (lower IQ, higher rates of ASD) and poorer social functioning in 22qDel may help explain this distinction from substance use patterns observed in CHR-P individuals.

Introduction: About 3.5 million people are living with schizophrenia in India, with most failing to receive minimally adequate care. Digital mental health applications could potentially decrease this treatment gap; however, these applications should be tailored to meet the needs and overcoming barriers of its end-users to ensure their adoption and sustained usage. Few studies in India have explored the perspectives of target stakeholders to understand how digital tools could be viable for supporting care. Therefore, this study explores the perceived needs and barriers of patients with schizophrenia, caregivers and clinicians in using digital mental health applications. Methods: Focus group discussions (FGDs) were conducted with patients having schizophrenia attending outpatient clinics at a government tertiary hospital, and their caregivers, and mental health clinicians in Bhopal, Madhya Pradesh, India. FGDs were audio-recorded and coded. Framework analysis was employed to guide the analysis, involving deductive and inductive generation of themes, data triangulation and comparison of perspectives between participant groups. Results: Six FGDs were conducted with individuals with schizophrenia (n ​= ​11), their caregivers (n ​= ​14), and mental health clinicians (n ​= ​19). Four a priori themes were established: a) Prior experiences with health applications; b) Content of a mental health application; c) Involvement of caregivers in mental health application usage and d) Supporting doctors’ work through mental health applications. Additionally, two themes were generated inductively: a) Qualities of a mental health application and b) Data privacy and confidentiality. Conclusion: Exploration of stakeholder perspectives on the content, features, and uses of mental health applications is crucial to yield initial insights about the use of these digital programs in India. This study generated a multitude of suggestions on app functionality and components, which can guide ongoing efforts to develop and deliver digital mental health applications for patients living with schizophrenia in low-resource settings, with limited access to mental health services.

Objective To examine feasibility and acceptability of smartphone mental health app use for symptom, cognitive, and digital phenotyping monitoring among people with schizophrenia in India and the United States. Methods Participants in Boston, USA and Bhopal and Bangalore, India used a smartphone app to monitor symptoms, play cognitive games, access relaxation and psychoeducation resources and for one month, with an initial clinical and cognitive assessment and a one-month follow-up clinical assessment. Engagement with the app was compared between study sites, by clinical symptom severity and by cognitive functioning. Digital phenotyping data collection was also compared between three sites. Results By Kruskal-Wallis rank-sum test, we found no difference between app activities completed or digital phenotyping data collected across the three study sites. App use also did not correlate to clinical or cognitive assessment scores. When using the app for symptom monitoring, preliminary findings suggest app-based assessment correlate with standard cognitive and clinical assessments. Conclusions Smartphone app for symptom monitoring and digital phenotyping for individuals with schizophrenia appears feasible and acceptable in a global context. Clinical utility of this app for real-time assessments is promising, but further research is necessary to determine the long-term efficacy and generalizability for serious mental illness.

Clozapine, a drug effective in treatment resistant schizophrenia, can modulate the brain’s electrical activity as measured by an electroencephalogram (EEG). Past reviews have focused on synthesizing literature related to epileptiform activity or rate of seizures in clozapine treated individuals. The aim of this review was to determine whether clozapine’s mediated effects on measurements related to neural oscillations can inform its therapeutic effects. Here, literature pertaining to studies that implemented pre-post designed investigations of clozapine and measured frequency characteristics of neural oscillations in individuals with schizophrenia were reviewed. The synthesis of findings suggests that while clozapine is associated with alterations in all neural oscillations, slower waves (delta and theta) are consistently increased in power by clozapine. We then further discuss potential mechanisms that may underlie these effects of clozapine. Future research can implement the findings of this review to motivate hypothesis-driven investigations into clozapine responsiveness biomarkers.

Abstract The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital phenotyping, or the use of integrated sensors to identify patterns in behavior and symptomatology, has shown potential in detecting subtle moment-to-moment changes. These changes, often referred to as anomalies, represent significant deviations from an individual’s baseline, may be useful in informing the risk of relapse in serious mental illness. Our investigation of smartphone-based anomaly detection resulted in 89% sensitivity and 75% specificity for predicting relapse in schizophrenia. These results demonstrate the potential of longitudinal collection of real-time behavior and symptomatology via smartphones and the clinical utility of individualized analysis. Future studies are necessary to explore how specificity can be improved, just-in-time adaptive interventions utilized, and clinical integration achieved.

Introduction: Optical coherence tomography (OCT) is used to study retinal structure in schizophrenia. Changes in retinal structure, especially the retinal nerve fiber layer (RNFL) has been correlated with psychotic disorders. Measurement variability is a concern since there are various generations of OCT devices. We investigated the inter- and intra-device agreement of macular thickness between spectral domain (SD−OCT) and swept source−OCT (SS−OCT), and compared macula and peripapillary group differences in schizophrenia using SS−OCT. Methods: Macular OCT thickness was obtained for schizophrenia (SZ, n = 30) and healthy controls (HC, n = 22) subjects using SD−OCT (Heidelberg Spectralis) and SS−OCT (DRI Topcon Triton). Peripapillary thickness was obtained using SS−OCT. RNFL, ganglion cell-inner plexiform complex (GCL+), RNFL plus GCL+ (GCL++), and macular thickness were collected. Clinical and cognitive data were gathered. All statistical analyses were performed using R software. Results: There was excellent inter-scanner agreement for GCL + and GCL++ with ICC’s between r = 0.92−0.99. Good-to-excellent intra-scanner (OD vs OS) agreement was present except for macular RNFL in the SS−OCT device. No significant peripapillary group differences were identified. Poorer GAF scores were correlated with thinner macular layers and thinner overall peripapillary retinal layer. Greater mania symptoms were associated with smaller peripapillary GCL + thickness (r=-0.43, p = 0.03). Poor overall cognition (Brief Assessment of Cognition in Schizophrenia) was associated with smaller overall peripapillary retinal thickness (r = 0.36, p = 0.02). Conclusion: While there is RNFL variability, GCL + and GCL++ are comparable between scanners SD−OCT and SS−OCT. Given that RNFL thinning is strongly implicated in psychotic disorders, the use of OCT scanners should not be interchanged due to increased RNFL measurement variability.

Instead of matching neuroimaging to static clinical targets, it is currently possible to use dynamic biobehavioral markers of cognition, functioning, behavior, and symptoms captured through a person’s smartphone. This paper reviews the published literature linking neuroimaging and smartphone data to understand the feasibility, methods, and potential of using smartphone sensing (often called digital phenotyping) as a target for neuroimaging. On June 30, 2020, a literature search was conducted on PubMed and PsycINFO for studies utilizing neuroimaging and smartphones tools. We excluded EEG focused studies, conference proceedings and abstracts. A snowball approach was applied to further locate papers. We utilized the NIMH’s Research Domain Criteria (RDoC) framework to organize results. 262 publications uncovered by the search were screened, and 14 papers were included in the final analysis. All studies differed in terms of the type of data collected from smartphones, type of neuroimaging used, areas of the brain measured, and population studied. The average duration before or after neuroimaging and smartphone assessments was 39 days. While it was not possible to directly compare studies, a majority of the included reports were classified under the Negative Valence Systems category in the RDoC framework. All studies reported statistically significant relationships between the information collected via the digital tool and the brain scans, and support feasibility of this method. The current literature connecting smartphone data and neuroimaging is nascent but holds the potential to better understand the ability of digital tools to inform brain structure and/or function. Although the protocols and studies from this search were heterogenous, results suggest feasibility and practicality of this work.

Abstract Background Despite significant advancements in healthcare technology, digital health solutions – especially those for serious mental illnesses – continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India. Methods Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality. Results Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement. Conclusion People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary – not substitutive – of therapeutic care from a clinician.

Background Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms. Aims In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis. Method Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains. Results The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups. Conclusions Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

There are currently many tools available for capturing and defining the context of one's environment. Digital phenotyping, the use of technology and sensors to capture moment-to-moment behavior, has shown potential in quantifying the lived experience of mental illness and in the identification of individualized targets related to recovery. Environmental data suggests that greenspace may have a restorative capacity on mental health. In this paper, we explore the relationship of greenspace derived from geolocation with self-reported symptomatology from individuals with schizophrenia as well as healthy controls. Individuals with schizophrenia had less exposure to greenspace than controls, but their exposure demonstrated a dosage effect: high greenspace environments were associated with lower symptoms for anxiety (Cohen's d = -0.70), depression (d = -0.97), and psychosis (d = -0.94), whereas effect sizes for healthy controls were all negligible or small (d < 0.38). The notion that greenspace may have a more pronounced effect on individuals with mental illness presents both potential areas for recovery as well as implications for health care policy, especially in cities with a broad range of greenspace environments.

Schizophrenia is associated with cognitive deficits across all stages of the illness (i.e., high risk, first episode, early and chronic phases). Identifying the underlying neurobiological mechanisms of these deficits is an important area of scientific inquiry. Here, we selectively review evidence regarding the pattern of deficits across the developmental trajectory of schizophrenia using the five cognitive domains identified by the Research Domain Criteria (RDoC) initiative. We also report associated findings from neuroimaging studies. We suggest that most cognitive domains are affected across the developmental trajectory, with corresponding brain structural and/or functional differences. The idea of a common mechanism driving these deficits is discussed, along with implications for cognitive treatment in schizophrenia.

Background: Although cognition is a core symptom of schizophrenia and associated with functional impairment, the degree of training for and time associated with its assessment makes it difficult to routinely monitor in clinic care.Smartphone based cognitive assessments could serve as a tool to measure cognition in real time as well as being easily scalable for broad use.Combined with other data gathered from smartphone sensors such as steps, sleep, and self-reported symptoms – capturing ‘cognition in context’ could provide a powerful new tool for assessing the functional burden of disease in schizophrenia. Methods: 18 participants with schizophrenia and 17 healthy controls completed novel cognitive assessments on their personal smartphones over the course of 12 weeks while also capturing self-reported surveys and step count. No payment or incentives were offered for engaging with the smartphone app. Differing levels of difficulty in cognitive tasks were tested and the results were modeled using a modified Cox proportional hazard model. Results: On the smartphone cognitive assessments that involved on simple patterns, both controls and those with schizophrenia achieved similar scores. On the more complex assessment that added task switching in addition to pattern recognition, those with schizophrenia achieved scores lower than controls. Collecting other forms of data such as surveys and steps was also feasible using the same smartphone platform. Discussion: It is feasible for those with schizophrenia to use their own smartphones to complete cognitive assessments and other measures related to their mental health. While we did not investigate the correlations between these cognitive assessments and other smartphone captured metrics like step count or self-reported symptoms, the potential to longitudinally assess cognition in the context of patients' environments outside of the clinic presents unique opportunities for characterizing cognitive burden in schizophrenia.

BackgroundAs mental healthcare expands to smartphone apps and other technologies that may offer therapeutic interventions without a therapist involved, it is important to assess the impact of non-traditional therapeutic relationships.AimsTo determine if there were any meaningful data regarding the digital therapeutic alliance in smartphone interventions for serious mental illnesses.MethodA literature search was conducted in four databases (PubMed, PsycINFO, Embase and Web of Science).ResultsThere were five studies that discuss the therapeutic alliance when a mobile application intervention is involved in therapy. However, in none of the studies was the digital therapeutic alliance the primary outcome. The studies looked at different mental health conditions, had different duration of technology use and used different methods for assessing the therapeutic alliance.ConclusionsAssessing and optimising the digital therapeutic alliance holds the potential to make tools such as smartphone apps more effective and improve adherence to their use. However, the heterogeneous nature of the five studies we identified make it challenging to draw conclusions at this time. A measure is required to evaluate the digital therapeutic alliance.