Neuroimaging alterations and relapse in early-stage psychosis.

BACKGROUND: Recent reports have indicated that symptom exacerbation after a period of improvement, referred to as relapse, in early-stage psychosis could result in brain changes and poor disease outcomes. We hypothesized that substantial neuroimaging alterations may exist among patients who experience relapse in early-stage psychosis. METHODS: We studied patients with psychosis within 2 years after the first psychotic event and healthy controls. We divided patients into 2 groups, namely those who did not experience relapse between disease onset and the magnetic resonance imaging (MRI) scan (no-relapse group) and those who did experience relapse between these 2 timings (relapse group). We analyzed 3003 functional connectivity estimates between 78 regions of interest (ROIs) derived from resting-state functional MRI data by adjusting for demographic and clinical confounding factors. RESULTS: We studied 85 patients, incuding 54 in the relapse group and 31 in the no-relapse group, along with 94 healthy controls. We observed significant differences in 47 functional connectivity estimates between the relapse and control groups after multiple comparison corrections, whereas no differences were found between the no-relapse and control groups. Most of these pathological signatures (64%) involved the thalamus. The Jonckheere-Terpstra test indicated that all 47 functional connectivity changes had a significant cross-group progression from controls to patients in the no-relapse group to patients in the relapse group. LIMITATIONS: Longitudinal studies are needed to further validate the involvement and pathological importance of the thalamus in relapse. CONCLUSION: We observed pathological differences in neuronal connectivity associated with relapse in early-stage psychosis, which are more specifically associated with the thalamus. Our study implies the importance of considering neurobiological mechanisms associated with relapse in the trajectory of psychotic disorders.

Smartphone keyboard dynamics predict affect in suicidal ideation.

While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (ß = -0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data.

Predicting Acute Changes in Suicidal Ideation and Planning: A Longitudinal Study of Symptom Mediators and the Role of the Menstrual Cycle in Female Psychiatric Outpatients With Suicidality.

OBJECTIVE: Cross-sectional and preliminary longitudinal findings suggest that cyclical ovarian hormone fluctuations influence acute suicide risk. The authors provide the first analyses in females with suicidality to investigate which daily symptoms covary with suicidal ideation and planning thoughts, the role of the menstrual cycle in daily symptom variation, how daily fluctuations in symptoms mediate the menstrual cycle-suicidality relationship, and how these associations vary across individuals. METHODS: Naturally cycling psychiatric outpatients (N=119) with past-month suicidal ideation provided daily ratings of psychiatric symptoms (depression, hopelessness, anxiety, feeling overwhelmed, agitation, anhedonia, worthlessness, rejection sensitivity, anger, perceived burdensomeness, and interpersonal conflict), suicidal ideation, and suicidal planning across at least one menstrual cycle. Symptom ratings were decomposed into trait (person-centered mean) and state (daily person-centered mean deviation) components. Five cycle phases were identified in relation to menses onset and ovulation (surge in urine luteinizing hormone level). Hypotheses were tested in multilevel structural equation models. RESULTS: Nearly all psychiatric symptoms covaried with fluctuations in daily suicidal ideation, and a limited set of symptoms (depression, hopelessness, rejection sensitivity, and perceived burdensomeness) predicted within-person increases in suicidal planning. Many patients demonstrated perimenstrual worsening of psychiatric symptoms, suicidal ideation, and suicidal planning. Depressive symptoms (depression, hopelessness, perceived burdensomeness, and anhedonia) were the most robust statistical mediators predicting perimenstrual exacerbation of suicidality. CONCLUSIONS: Research on the menstrual cycle and suicide has been limited historically by small, cross-sectional samples. This study provides the first evidence that measuring day-to-day correlates of suicidality in a large transdiagnostic sample of females with suicidal ideation can contribute to understanding the pathways by which the menstrual cycle influences acute suicide risk.

Smartphone keyboard dynamics predict affect in suicidal ideation.

While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (ß = -0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data.

Alcohol use and motives for drinking across the menstrual cycle in a psychiatric outpatient sample.

BACKGROUND: Females who misuse alcohol experience high rates of negative physical and mental health consequences. Existing findings are inconsistent but suggest a relationship between ovarian hormones and alcohol use. We aim to clarify how alcohol use and drinking motives vary across the menstrual cycle in female psychiatric outpatients using the luteinizing hormone (LH)-confirmed cycle phase. METHODS: Daily self-reports (n = 3721) were collected from 94 naturally cycling females, recruited for past-month suicidal ideation, during the baseline phase of three parent clinical trials between February 2017 and May 2022. Multilevel logistic and linear models estimated the relationship between the cycle phase (with LH-surge confirmed ovulation) and daily alcohol use or drinking motives, moderated by the weekend. Models were adjusted for age, legal drinking status, substance use disorder, and the COVID-19 pandemic, and included random effects. RESULTS: Participants were generally more likely to drink in the midluteal (vs. perimenstrual) phase, but more likely to drink heavily on weekends in periovulatory and perimenstrual (vs. midluteal) phases. Social motives for drinking were significantly higher on weekends in the periovulatory, mid-follicular, and midluteal phases (vs. weekdays), but this finding was non-significant in the perimenstrual phase. Participants rated drinking to cope higher in the perimenstrual phase (vs. midluteal phase), regardless of the weekend. CONCLUSION: In a psychiatric sample with LH-surge-confirmed ovulation, we find an increased likelihood to drink heavily in periovulatory and perimenstrual phases on weekends. We also find that the perimenstrual phase is associated with increased drinking to cope, and relatively lower weekend social drinking. Finally, random effects across models suggest individual differences in the extent to which the cycle influences drinking. Our findings stress (1) predictable phases of increased high-risk alcohol use across the menstrual cycle, and (2) the importance of individual assessment of cyclical changes in alcohol use to predict and prevent ovulation- and menses-related surges in heavy drinking.

A multimodal study of a first episode psychosis cohort: potential markers of antipsychotic treatment resistance.

Treatment resistant (TR) psychosis is considered to be a significant cause of disability and functional impairment. Numerous efforts have been made to identify the clinical predictors of TR. However, the exploration of molecular and biological markers is still at an early stage. To understand the TR condition and identify potential molecular and biological markers, we analyzed demographic information, clinical data, structural brain imaging data, and molecular brain imaging data in 7 Tesla magnetic resonance spectroscopy from a first episode psychosis cohort that includes 136 patients. Age, gender, race, smoking status, duration of illness, and antipsychotic dosages were controlled in the analyses. We found that TR patients had a younger age at onset, more hospitalizations, more severe negative symptoms, a reduction in the volumes of the hippocampus (HP) and superior frontal gyrus (SFG), and a reduction in glutathione (GSH) levels in the anterior cingulate cortex (ACC), when compared to non-TR patients. The combination of multiple markers provided a better classification between TR and non-TR patients compared to any individual marker. Our study shows that ACC-GSH, HP and SFG volumes, and age at onset, could potentially be biomarkers for TR diagnosis, while hospitalization and negative symptoms could be used to evaluate the progression of the disease. Multimodal cohorts are essential in obtaining a comprehensive understanding of brain disorders.