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Abstract Functional impairments contribute to poor quality of life in schizophrenia spectrum disorders (SSD). We sought to (Objective I) define the main functional phenotypes in SSD, then (Objective II) identify key biopsychosocial correlates, emphasizing interpretable data-driven methods. Objective I was tested on independent samples: Dataset I (N = 282) and Dataset II (N = 317), with SSD participants who underwent assessment of multiple functioning areas. Participants were clustered based on functioning. Objective II was evaluated in Dataset I by identifying key features for classifying functional phenotype clusters from among 65 sociodemographic, psychological, clinical, cognitive, and brain volume measures. Findings were replicated across latent discriminant analyses (LDA) and one-vs.-rest binomial regularized regressions to identify key predictors. We identified three clusters of participants in each dataset, demonstrating replicable functional phenotypes: Cluster 1—poor functioning across domains; Cluster 2—impaired Role Functioning, but partially preserved Independent and Social Functioning; Cluster 3—good functioning across domains. Key correlates were Avolition, anhedonia, left hippocampal volume, and measures of emotional intelligence and subjective social experience. Avolition appeared more closely tied to role functioning, and anhedonia to independent and social functioning. Thus, we found three replicable functional phenotypes with evidence that recovery may not be uniform across domains. Avolition and anhedonia were both critical but played different roles for different functional domains. It may be important to identify critical functional areas for individual patients and target interventions accordingly.

Interleaved learning in machine learning algorithms is a biologically inspired training method with promising results. In this short note, we illustrate the interleaving mechanism via a simple statistical and optimization framework based on Kalman Filter for Linear Least Squares.

ObjectiveThis study examines the quality of care provided through telepsychiatry by comparing psychiatric hospitalization rates among patients receiving in-person psychiatric care prior to the COVID-19 pandemic with rates among patients receiving virtual psychiatric care during the COVID-19 pandemic.MethodsMental health-related hospitalization rates among patients enrolled in a large academic hospital’s outpatient psychiatry programs between March 1, 2018 and February 28, 2022 were retrospectively analyzed. Four time periods were created, spanning March 1 to February 28 of the following year. Demographic and clinical data were collected from the electronic health record, and descriptive statistics were calculated. Change in hospitalization rate between time periods was evaluated using McNemar’s test.ResultsIn the 2018 time period, 7.38% of all enrolled patients were hospitalized, compared to 7.70% hospitalized in the 2019 period, 5.74% in the 2020 period, and 5.38% in the 2021 period. Patients enrolled in both the 2018 and the 2019 periods saw no difference in hospitalization rate between the 2 years (2.93% in 2018, 2.83% in 2019; p = 0.830); patients enrolled in both 2019 and 2020 saw significantly lower hospitalization rates in 2020 (5.47% in 2019, 4.58% in 2020; p = 0.022); and patients enrolled in both 2020 and 2021 saw no difference (3.34% in 2020, 3.23% in 2021; p = 0.777).ConclusionPsychiatric hospitalization rates significantly decreased between the 2019 and the 2020 periods, suggesting a decrease in admissions associated with adoption of telepsychiatry. Future research should differentiate the roles played by telepsychiatry and COVID-19-related factors in reducing hospitalization rates during the pandemic.

Abstract Social cognitive impairments are core features of schizophrenia spectrum disorders (SSD) and are associated with greater functional impairment and decreased quality of life. Metabolic disturbances have been related to greater impairment in general neurocognition, but their relationship to social cognition has not been previously reported. In this study, metabolic measures and social cognition were assessed in 245 participants with SSD and 165 healthy comparison subjects (HC), excluding those with hemoglobin A1c (HbA1c) > 6.5%. Tasks assessed emotion processing, theory of mind, and social perception. Functional connectivity within and between social cognitive networks was measured during a naturalistic social task. Among SSD, a significant inverse relationship was found between social cognition and cumulative metabolic burden (β = −0.38, p

Regularization methods are often employed to reduce overfitting of machine learning models. Nonconvex penalty functions are often considered for regularization because of their near-unbiasedness properties. In this paper, we consider two relatively new penalty functions: Laplace and arctan, and show how they fit into certain recently introduced statistical and optimization frameworks. We also compare empirically the performance of the two new penalty functions with existing penalty functions utilized as regularizers of deep neural networks and convolutional neural networks on seven different datasets.

Parasympathetic arousal is associated with states of heightened attention and well-being. Arousal may affect widespread cortical and subcortical systems across the brain, however, little is known about its influence on cognitive task processing and performance. In the current study, healthy adult participants (n ​= ​20) underwent multi-band echo-planar imaging (TR ​= ​0.72 ​s) with simultaneous pulse oximetry recordings during performance of the Multi Source Interference Task (MSIT), the Oddball Task (OBT), and during rest. Processing speed on both tasks was robustly related to heart rate (HR). Participants with slower HR responded faster on both the MSIT (33% variance explained) and the OBT (25% variance explained). Within all participants, trial-to-trial fluctuations in processing speed were robustly related to the heartbeat-stimulus interval, a metric that is dependent both on the concurrent HR and the stimulus timing with respect to the heartbeat. Models examining the cardiac-BOLD response revealed that a distributed set of regions showed arousal-related activity that was distinct for different task conditions. Across these cortical regions, activity increased with slower HR. Arousal-related activity was distinct from task-evoked activity and it was robust to the inclusion of additional physiological nuisance regressors into the models. For the MSIT, such arousal-related activity occurred across visual and dorsal attention network regions. For the OBT, this activity occurred within fronto-parietal regions. For rest, arousal-related activity also occurred, but was confined to visual regions. The pulvinar nucleus of the thalamus showed arousal-related activity during all three task conditions. Widespread cortical activity, associated with increased parasympathetic arousal, may be propagated by thalamic circuits and contributes to improved attention. This activity is distinct from task-evoked activity, but affects cognitive performance and therefore should be incorporated into neurobiological models of cognition and clinical disorders.

Dynamic correlation is the correlation between two time series across time. Two approaches that currently exist in neuroscience literature for dynamic correlation estimation are the sliding window method and dynamic conditional correlation. In this paper, we first show the limitations of these two methods especially in the presence of extreme values. We present an alternate approach for dynamic correlation estimation based on a weighted graph and show using simulations and real data analyses the advantages of the new approach over the existing ones. We also provide some theoretical justifications and present a framework for quantifying uncertainty and testing hypotheses.