Amotivational states and insufficient recruitment of mental effort have been observed in a variety of clinical populations, including depression, traumatic brain injury, post-traumatic stress disorder, and attention deficit hyperactivity disorder. Previous rodent models of effort-based decision making have utilized physical costs whereas human studies of effort are primarily cognitive in nature, and it is unclear whether the two types of effortful decision making are underpinned by the same neurobiological processes. We therefore designed a novel rat cognitive effort task (rCET) based on the 5-choice serial reaction time task, a well-validated measure of attention and impulsivity. Within each trial of the rCET, rats are given the choice between an easy or hard visuospatial discrimination, and successful hard trials are rewarded with double the number of sugar pellets. Similar to previous human studies, stable individual variation in choice behavior was observed, with 'workers' choosing hard trials significantly more than their 'slacker' counterparts. Whereas workers 'slacked off' in response to administration of amphetamine and caffeine, slackers 'worked harder' under amphetamine, but not caffeine. Conversely, these stimulants increased motor impulsivity in all animals. Ethanol did not affect animals' choice but invigorated behavior. In sum, we have shown for the first time that rats are differentially sensitive to cognitive effort when making decisions, independent of other processes such as impulsivity, and these baseline differences can influence the cognitive response to psychostimulants. Such findings could inform our understanding of impairments in effort-based decision making and contribute to treatment development.

Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.

Background Digital phenotyping is the use of data from smartphones and wearables collected in situ for capturing a digital expression of human behaviors. Digital phenotyping techniques can be used to analyze both passively (e.g., sensor) and actively (e.g., survey) collected data. Machine learning offers a possible predictive bridge between digital phenotyping and future clinical state. This review examines passive digital phenotyping across the schizophrenia spectrum and bipolar disorders, with a focus on machine-learning studies. Methods A systematic review of passive digital phenotyping literature was conducted using keywords related to severe mental illnesses, data-collection devices (e.g., smartphones, wearables, actigraphy devices), and streams of data collected. Searches of five databases initially yielded 3312 unique publications. Fifty-one studies were selected for inclusion, with 16 using machine-learning techniques. Results All studies differed in features used, data pre-processing, analytical techniques, algorithms tested, and performance metrics reported. Across all studies, the data streams and other study factors reported also varied widely. Machine-learning studies focused on random forest, support vector, and neural net approaches, and almost exclusively on bipolar disorder. Discussion Many machine-learning techniques have been applied to passively collected digital phenotyping data in schizophrenia and bipolar disorder. Larger studies, and with improved data quality, are needed, as is further research on the application of machine learning to passive digital phenotyping data in early diagnosis and treatment of psychosis. In order to achieve greater comparability of studies, common data elements are identified for inclusion in future studies.