Attention Dysregulation in Breast Cancer Patients Following a Complementary Alternative Treatment Routine: A Double-Blind Randomized Trial.

INTRODUCTION: Breast cancer patients and survivors frequently report fatigue, emotional, and cognitive disturbances, which reduce performance at all levels of occupation and make life quality issues a considerable clinical concern. The aim of this study is to evaluate attention and emotion regulation across radiotherapy period and the possible effects of complementary alternative medicine (CAM). METHODS: Fifty-seven patients with unilateral breast cancer underwent surgery and systemic chemotherapy before participating in this double-blind randomized study. Two thirds were given CAM (n = 38) while the rest received placebo (carrier only, n = 19). Patients' attention and anxiety were physiologically tested at baseline, 2 and 4 weeks during the radiation period as well as 1-month after the end of radiation session. RESULTS: Both groups showed similar levels of anxiety with no significant differences at baseline nor post-radiotherapy. Long-term significant recovery of attention performance was observed in the CAM patients, accompanied by a similar tendency in anxiety level, measured by the eye-blink probability. CONCLUSIONS: This study physiologically validates the attention impairment reported among breast cancer survivors; also, it depicted a beneficial late-effect of a routine CAM on attention dysregulation. The suggested non-invasive physiological measures can physiologically monitor patients' psychological and cognitive well-being as well as evaluate the beneficial effect of CAM in breast cancer patients by assessing their coping ability to support the treatment plan. Thus, the results have potential clinical implications on patients' and survivors' quality of life. TRIAL REGISTRATION: NIH, NCT02890316. Registered July 2016, http://www.ClinicalTrials.gov.

High resolution behavioral and neural activity representation using a geometrical approach.

Available tools for recording neuronal activity are limited and reductive due to massive data arising from high-frequency measurements. We have developed a method that utilizes variance within the physiological activity and includes all data points per measurement. Data is expressed geometrically in a physiologically meaningful manner, to represent a precise and detailed view of the recorded neural activity. The recorded raw data from any pair of electrodes was plotted and following a covariance calculation, an eigenvalues and chi-square distribution were used to define the ellipse which bounds 95% of the raw data. We validated our method by correlating specific behavioral observation and physiological activity with behavioral tasks that require similar body movement but potentially involve significantly different neuronal activity. Graphical representation of telemetrically recorded data generates a scatter plot with distinct elliptic geometrical properties that clearly and significantly correlated with behavior. Our reproducible approach improves on existing methods by allowing a dynamic, accurate and comprehensive correlate using an intuitive output. Long-term, it may serve as the basis for advanced machine learning applications and animal-based artificial intelligence models aimed at predicting or characterizing behavior.