IMPORTANCE Autoimmune epilepsy is an underrecognized condition, and its true incidence is unknown. Identifying patients with an underlying autoimmune origin is critical because these patients' condition may remain refractory to conventional antiseizure medications but may respond to immunotherapy.OBJECTIVE To determine the prevalence of neurological autoantibodies (Abs) among adult patients with epilepsy of unknown etiology. DESIGN, SETTING, AND PARTICIPANTSConsecutive patients presenting to neurology services with new-onset epilepsy or established epilepsy of unknown etiology were identified. Serum samples were tested for autoimmune encephalitis Abs as well as thyroperoxidase (TPO) and glutamic acid decarboxylase 65 (GAD65) Abs. An antibody prevalence in epilepsy (APE) score based on clinical characteristics was assigned prospectively. Data were collected from June 1, 2015, to June 1, 2016. MAIN OUTCOMES AND MEASURESPresence of neurological Abs. A score based on clinical characteristics was assigned to estimate the probability of seropositivity prior to antibody test results. Good seizure outcome was estimated on the basis of significant reduction of seizure frequency at the first follow-up or seizure freedom. RESULTSOf the 127 patients (68 males and 59 females) enrolled in the study, 15 were subsequently excluded after identification of an alternative diagnosis. Serum Abs suggesting a potential autoimmune etiology were detected in 39 (34.8%) cases. More than 1 Ab was detected in 7 patients (6.3%): 3 (2.7%) had TPO-Ab and voltage-gated potassium channel complex (VGKCc) Ab, 2 (1.

As the potential of smartphone apps and sensors for healthcare and clinical research continues to expand, there is a concomitant need for open, accessible, and scalable digital tools. While many current app platforms offer useful solutions for either clinicians or patients, fewer seek to serve both and support the therapeutic relationship between them. Thus, we aimed to create a novel smartphone platform at the intersection of patient demands for trust, control, and community and clinician demands for transparent, data driven, and translational tools. The resulting LAMP platform has evolved through numerous iterations and with much feedback from patients, designers, sociologists, advocates, clinicians, researchers, app developers, and philanthropists. As an open and free tool, the LAMP platform continues to evolve as reflected in its current diverse use cases across research and clinical care in psychiatry, neurology, anesthesia, and psychology. In this paper, we explore the motivation, features, current progress, and next steps to pair the platform for use in a new digital psychiatry clinic, to advance digital interventions for youth mental health, and to bridge gaps in available mental health care for underserved patient groups. The code for the LAMP platform is freely shared with this paper to encourage others to adapt and improve on our team's efforts.

Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help—the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students.

Digital health technologies such as smartphones present the potential for increased access to care and on-demand services. However, many patients with serious mental illnesses (eg, schizophrenia) have not been offered the digital health training necessary to fully utilize these innovative approaches. To bridge this digital divide in knowledge and skills, we created a hands-on and interactive training program grounded in self-determination theory, technology use cases, and the therapeutic alliance. This article introduces the need and theoretical foundation for and the experience of running the resulting Digital Opportunities for Outcomes in Recovery Services (DOORS) group in the setting of 2 programs: a first-episode psychosis program and a clubhouse for individuals with serious mental illness. The experience of running these 2 DOORS groups resulted in 2 publicly available, free training manuals to empower others to run such groups and adapt them for local needs. Future work on DOORS will expand the curriculum to best support digital health needs and increase equity of access to and knowledge and skills related to technology use in serious mental illness.

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This study highlights certain important clinical and electrographic aspects of autoimmune epilepsy, and the significance of early diagnosis and initiation of immunomodulatory therapy.

Sudden unexpected death in epilepsy (SUDEP) is a significant cause of death for people with epilepsy. Recent research suggests that multiple factors may contribute and that both cardiac and respiratory mechanisms are involved. Both human and animal data suggest that specific genetic factors might play a role in some cases. Recent animal data suggest that serotonin might affect respiratory mechanisms and may be involved. Both cardiac and respiratory abnormalities are more likely with generalized tonic-clonic seizures. Uncontrolled epilepsy, particularly with generalized tonic-clonic seizures, appears to be the most highly associated modifiable risk factor for SUDEP.

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