JMIR Neurotechnology

While changes in brain structure are common across the lifespan, it is difficult to differentiate benign variations from early disease pathogenesis, especially in patients participating in home-based rehabilitation. Cognitive decline is frequently linked with normative aging, but its early detection can facilitate preventative interventions, particularly in patients at high risk of cognitive impairment and dementia, such as older women. Although women have fewer modifiable risk factors for dementia than men, wearables have the potential to establish new digital endpoints that facilitate the management and/or prevention of abnormal brain aging. Sleep, which is necessary for maintaining overall brain health, is one behavior tracked via wearables, for which an ever-growing body of pilot and validation phase studies exists, yet endpoints defining optimal sleep are not one-size-fits-all, as individual chronotype variability necessitates new strategies to personalize care. Though sex-based differences in circadian rhythm are well established, little is understood about which sleep measures and thresholds are uniquely important to cognitive health in women, particularly those with high comorbid burden. In this viewpoint, we discuss recent findings on the use of wearables to track sleep and cognitive health in women, while highlighting challenges and opportunities for health outcomes and clinical trial researchers seeking to implement meaningful digital endpoints in future telerehabilitation programs.

The need to observe human cognition in more natural environments, i.e., outside of laboratory settings, is critical to understanding human cognition and behavior. While wearable low-cost neuroimaging modalities (electroencephalography-EEG/functional near infrared spectroscopy-fNIRS) have gained traction for their portability, and robustness to motion artifacts, there is a lack of portable tools for study in ecologically valid settings. Smartphones offer a solution, their ubiquity, wireless connectivity make them candidates for “Pocket Labs” that travel with subjects. Yet, few mobile applications integrate sensing modalities, those that do are limited in research use. We developed the Wearable Cognitive Assessment and Augmentation Toolkit (WearCAAT), a cross-platform mobile application for task-based functional neuroimaging. We are currently collecting data with WearCAAT in a clinical setting; however, we first need to validate our implementation with healthy human participants.

Optimizing rehabilitation intensity using a Robotic-Assisted Hand Rehabilitation Exercise (RAHRE) coupled with a virtual environment is a promising intervention as it aligns with key neuroplasticity principles.

Migraine is a debilitating neurological condition often impacting quality of life and resulting in physical, emotional, and social burdens. Pharmaceutical interventions are the conventional treatment for migraine; however, behavioural interventions provide safe alternatives. Both mindfulness meditation and neurofeedback are behavioural interventions that have been separately studied for migraine treatment. To date, no studies have investigated neurofeedback-assisted mindfulness meditation for migraine treatment and prevention.

Pediatric and adolescent patients with attention deficit hyperactivity disorder (ADHD) present unique challenges as it pertains to adherence to device-based therapies outside the clinical environment. The development, approval, and availability of neurostimulation devices for the treatment of ADHD has prompted extraclinical (outside the sphere of the clinic) research on the real-world implementation of such therapies in a population that has difficulty remembering tasks and staying attentive to therapy.

Neuromodulation of the auricular branch of the vagus nerve using low intensity focused ultrasound (LIFU) is an emerging mode of treatment for anxiety that could provide a complementary or alternative treatment modality for individuals that are refractory to conventional interventions. The proposed benefits of this technology have been largely unexamined with clinical populations. Further research is required to understand its clinical potential and utility in improving and managing moderate to severe symptoms.

A 12-month longitudinal observational study was conducted on 43 children aged 2-18 years to evaluate the effectiveness of the Cognitivebotics AI-based platform in conjunction with continuous therapy, in improving therapeutic outcomes for subjects with Autism Spectrum Disorder (ASD).

This paper presents an easy-to-use, affordable Rehabilitation Manipulandum Device (RMD) equipped with smart monitoring and assistive technologies to engage in game-based exercise and repetitive task practice. The RMD has been designed to enhance a wide range of fine motor manual dexterity skills, including thumb, finger, and wrist movements. By focusing on finger and hand functions, it extends its utility beyond basic reaching or object transfer movements. Various interchangeable 3D-printed therapy handles of different shapes and sizes can be easily attached to the RMD drive shaft. These handle movements can be utilized to engage with numerous affordable, commercially available computer games, allowing patients to practice tasks that involve varying movement amplitudes, speeds, precision, and cognitive challenges. Additionally, the device is capable of automatically recording and storing the patient's real-time performance data on any given computer, integrating assessment into treatment.

Speech features are increasingly linked to neurodegenerative and mental health conditions, offering the potential for early detection and differentiation between disorders. As interest in speech analysis grows, distinguishing between conditions becomes critical for reliable diagnosis and assessment.

Background: The emergence of generative artificial intelligence (GenAI) presents unprecedented opportunities to redefine conceptions of personhood and cognitive disability, potentially enhancing the inclusion and participation of individuals with cognitive disabilities in society.

Interest in using digital sensors to monitor post-stroke patients (PSPs) for depression, a risk factor for poor outcomes, has grown rapidly; however, little is known about behavioral phenotypes related to future mood and if patients with and without previously diagnosed depression experience similar phenotypes.

Virtual approaches to workplace concussion assessment have demonstrated value to end users. The feasibility of administering physical concussion assessment measures in a virtual context has been minimally explored and there is limited information on important psychometric properties of physical assessment measures used in virtual contexts.