Using transcranial direct-current stimulation (tDCS) to understand cognitive processing

Noninvasive brain stimulation methods are becoming increasingly common tools in the kit of the cognitive scientist. In particular, transcranial direct-current stimulation (tDCS) is showing great promise as a tool to causally manipulate the brain and understand how information is processed. The popularity of this method of brain stimulation is based on the fact that it is safe, inexpensive, its effects are long lasting, and you can increase the likelihood that neurons will fire near one electrode and decrease the likelihood that neurons will fire near another. However, this method of manipulating the brain to draw causal inferences is not without complication. Because tDCS methods continue to be refined and are not yet standardized, there are reports in the literature that show some striking inconsistencies. Primary among the complications of the technique is that the tDCS method uses two or more electrodes to pass current and all of these electrodes will have effects on the tissue underneath them. In this tutorial, we will share what we have learned about using tDCS to manipulate how the brain perceives, attends, remembers, and responds to information from our environment. Our goal is to provide a starting point for new users of tDCS and spur discussion of the standardization of methods to enhance replicability.The authors declare that they had no conflicts of interest with respect to their authorship or the publication of this article. This work was supported by grants from the National Institutes of Health (R01-EY019882, R01-EY025272, P30-EY08126, F31-MH102042, and T32-EY007135). (R01-EY019882 - National Institutes of Health; R01-EY025272 - National Institutes of Health; P30-EY08126 - National Institutes of Health; F31-MH102042 - National Institutes of Health; T32-EY007135 - National Institutes of Health)Accepted manuscrip

Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson’s Disease

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson’s disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies

Replacing sedentary time with physical activity or sleep: effects on cancer-related cognitive impairment in breast cancer survivors

Abstract Background Evidence suggests reallocating daily sedentary time to physical activity or sleep confers important health benefits in cancer survivors. Despite emerging research suggesting physical activity as a treatment for cancer-related cognitive impairment (CRCI), little is known about the interactive effects of behaviors across the 24-h period. The present purpose was to examine the cognitive effects of reallocating sedentary time to light-intensity physical activity, moderate-to-vigorous physical activity (MVPA), or sleep in breast cancer survivors. Methods Breast cancer survivors (N = 271, Mage = 57.81 ± 9.50 years) completed iPad-based questionnaires and cognitive tasks assessing demographics, health history, executive function, and processing speed (Task-Switch, Trail Making). Participants wore an accelerometer for seven consecutive days to measure their sedentary, physical activity, and sleep behaviors. Single effects (each behavior individually) and partition (controlling for other behaviors) models were used to examine associations among behaviors and cognitive performance. Isotemporal substitution models were used to test the cognitive effects of substituting 30 min of sedentary time with 30 min of light-intensity activity, MVPA, and sleep. Results MVPA was associated with faster Task-switch reaction time in the partition models (stay: B = − 35.31, p = 0.02; switch: B = − 48.24, p = 0.004). Replacing 30 min of sedentary time with 30 min of MVPA yielded faster reaction times on Task-Switch stay (B = − 29.37, p = 0.04) and switch (B = − 39.49, p = 0.02) trials. In Trails A single effects models, sedentary behavior was associated with faster completion (B = − 0.97, p = 0.03) and light-intensity activity with slower completion (B = 1.25, p = 0.006). No single effects were observed relative to Trails B completion (all p > 0.05). Only the effect of MVPA was significant in the partition models (Trails A: B = − 3.55, p = 0.03; Trails B: B = − 4.46, p = 0.049). Replacing sedentary time with light-intensity activity was associated with slower Trails A (B = 1.55 p = 0.002) and Trails B (B = 1.69, p = 0.02) completion. Replacing light activity with MVPA yielded faster Trails A (B = − 4.35, p = 0.02) and Trails B (B = − 5.23, p = 0.03) completion. Conclusions Findings support previous research suggesting MVPA may be needed to improve cognitive function in breast cancer survivors. Trails findings underscore the need to dissect sedentary contexts to better understand the impact of daily behavioral patterns on CRCI. Additional research investigating the cognitive impacts of behaviors across the 24-h period is warranted. Trial registration This study is registered with United States ClinicalTrials.gov (NCT02523677; 8/14/2015)

Noninvasive targeted neuromodulation and functional imaging in behaving macaques

All presently available neural stimulation methods are either invasive or can only be moderately localized, and a neurostimulation method that could overcome these limitations would be invaluable for the mapping of brain circuits, disease diagnosis in the brain, neurosurgery and therapy. Neural stimulation with magnetic resonance guided focused ultrasound (MRgFUS) is a promising technology that can noninvasively excite or inhibit neural activity in well-defined discrete volumes of the brain, subsequently enabling investigation of brain circuits with magnetic resonance imaging (MRI). In this study, we seek to explore ultrasonic neuromodulation in the frontal eye field of a macaque monkey, while measuring the effects of neuromodulation via eventrelated potentials, behavioral responses, and blood oxygen level dependent functional MRI

Additional file 1: of Replacing sedentary time with physical activity or sleep: effects on cancer-related cognitive impairment in breast cancer survivors

Supplementary File (Additional Questionnaires). (PDF 78 kb

Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson’s Disease

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson’s disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies

Identifying and characterising sources of variability in digital outcome measures in Parkinson’s disease

Abstract Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson’s disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies