Brain Network Analytics (BNA) in the Psychiatric Practice

INTRODUCTION

PSYCHIATRIC CARE AND EEG

Psychiatric disorders have a significant impact on individuals and families, but many do not receive adequate care. Routine electroencephalography (EEG) could improve disease management in the psychiatry by providing valuable information about brain function that can assist in diagnosis, treatment planning, and monitoring. EEG is a non-invasive and relatively inexpensive tool that measures the brain’s electrical activity and can detect changes in brain wave patterns that may be associated with certain psychiatric conditions. For example, abnormal EEG findings have been reported in individuals with general anxiety disorder 2, depression 3–5, and attention deficit disorder (ADHD) 6,7 and may be used to inform treatment decisions and personalize care. Additionally, EEG has the potential to be used as a biomarker for predicting treatment response8,9 and monitoring the effects of interventions and diseases on brain function10,11. However, extensive preprocessing, training, and expertise required to interpret EEG data can be a barrier to its widespread use in clinical settings. In addition to the challenge of expert interpretation, the variability in brain wave patterns across individuals can make it difficult to discern abnormal findings, in the absence of normative data.

BNA – TOWARDS A CHANGE IN PSYCHIATRIC CARE

Brain Network Analysis (BNA) technology offers a promising solution to the challenge of widespread use of EEG in psychiatric practice. BNA employs sophisticated algorithms to automatically analyze EEG recordings taken during rest and cognitive tasks, producing easy-to-interpret results for quantitative EEG (QEEG) and event-related potentials (ERP) analysis. By automatically providing insights into electrophysiology and the associated cognitive functioning, BNA eliminates the need for preprocessing, manual analysis, or advanced interpretation skills. Additionally, BNA offers a significant advantage by comparing a patient’s EEG to a comprehensive normative dataset of healthy individuals of the same age, allowing clinicians to draw meaningful conclusions quickly.