BNA

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The human brain is a highly complex multilayered organ composed of many billions of neurons, organized into very complicated interconnecting neural networks. Typically, each neuron is connected to tens of thousands of other neurons through connections called synapses. Electrochemical signals that are passed between neurons through these synapses allow them to communicate. The connections between neurons are not static, but change over time. The more signals sent between two neurons, the stronger the connection grows, and so, with each new experience, the brain slightly rewires its physical and functional structure.

Unique local physical and functional connections between neurons are called neural networks. Neural networks are typically characterized by preferred signaling pathways, and it is the interactions within and between these networks of neurons that enable us to perform various functions including cognitive functions, such as attention, working memory, pattern recognition and problem-solving.

It is this simultaneous cooperative function of brain areas working together as large-scale networks which is at the root of the sophistication and computational power of the human brain.

ElMindA’s BNA™ technology is based on non-invasive recordings of multi-channel EEG event-related potentials (ERPs), and a comprehensive multi-dimensional analysis of such recordings by advanced algorithms, aimed at understanding and visualizing the network complexity (or Brain Networks Activation patterns) of brain function, dysfunction, disease progression and response to therapeutic interventions.   BNA™ takes cognitive-electrophysiology (ERPs), a direct measure of neural activity associated with cognitive function, to a new frontier, unparalleled by EEG, QEEG or ERP alone or by any other anatomic and functional brain imaging and evaluation tools.

The BNA™ algorithms use innovative sets of signal processing and pattern recognition techniques to seek and map activated neural pathways in task-related data points with respect to time, location, amplitude and frequency. By projecting the individual data points into clusters, BNA™ reveals three-dimensional images of Brain Network Activation patterns (or BNAs in short) which represent high resolution functional neural pathways. These BNA™ network patterns and scores can aid clinicians with profiling of brain functionality, changes in functionality and/or dysfunctionality and can assist follow-up of changes in disease progression and/or response to therapeutic interventions.

BNA™ reports can be generated by the user for a specific patient. The BNA™ report details both quantitative and qualitative data in the form of graphs, score tables and brain maps.  These BNA™ scores and data are shown over time enabling clinicians to follow up on changes due to disease progression or recovery and/or response to therapeutic interventions over time.

The following example schematically illustrates a BNA™ pattern associated with a specific cognitive function (functional pattern), a dysfunctional BNA™ pattern of the same cognitive function, and the change of the BNA™ pattern induced by a therapeutic intervention: