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Abstraction has long been a valuable tool for scientists and engineers to simplify and understand complex systems. This approach has brought significant progress across various fields. For instance, beginner electrical engineering students are introduced to resistance by ignoring factors like heat conduction, wire induction, and capacitance, which would otherwise complicate their understanding of this key concept. Similarly, physics students often first study falling objects by considering only the effect of gravity and disregarding air resistance, making it easier to grasp the basic principles of motion. In computer engineering, the initial study of operational amplifiers assumes ideal conditions such as infinite open-loop gain and input resistance, no current draw, and uniform bandwidth—assumptions that are not true in practice but help simplify early learning before delving into more complex realities. These kinds of idealized models are also common in biology and medicine. For example, diseases are often taught as clearly defined entities, even though real-life diagnosis and treatment must consider the unique variations in each individual’s biology and pathology. As Hippocrates famously emphasized, understanding a patient requires more than just knowing the disease—they must understand the person who has it.

 Volume II of Brain and Human Body Modeling is organized into seven major sections, each focusing on a distinct area of research and innovation.

Part 1 explores Tumor-Treating Fields (TTFields)—a novel and FDA-approved therapy for glioblastoma. It offers a detailed overview of TTFields, including the biological basis of tumor development and a variety of treatment approaches, ranging from placing electrodes on the scalp to more invasive procedures like removing skull sections for better access. Accurate modeling and advanced simulation techniques are essential for developing and evaluating these treatment strategies.

Part 2 focuses on non-invasive brain stimulation, particularly techniques like Transcranial Direct Current Stimulation (tDCS). It discusses recent advancements in modeling electric field strength and direction, the use of white matter tractography, and strategies for customizing multi-electrode setups for Transcranial Electrical Stimulation. These brain models are becoming increasingly sophisticated as more high-resolution medical data becomes available.

Part 3 shifts the focus to non-invasive stimulation of the spinal cord and peripheral nervous system. One chapter addresses electrical and magnetic spinal stimulation, targeting sensory and motor pathways, while also modeling electric field distributions. Another chapter introduces a highly focused, small-scale magnetic stimulator designed for use on peripheral nerves.

Part 4 addresses neurophysiological signal modeling. It begins with efforts to integrate electromagnetic and hemodynamic imaging for a multi-modal neuroimaging approach. Following that, a new simulation method is introduced that offers exceptional precision and speed. This method is then applied to the cerebral cortex to explore the potential of multiscale brain modeling.

Part 5 investigates neural circuit modeling at both micro and macro scales. It starts by defining key concepts and methods for modeling large and complex neural networks. Subsequent chapters examine the modeling of specific retinal ganglion cells and delve into brain connectivity using data from the Human Connectome Project.

Part 6 presents models for studying the effects of high and radio frequencies. Topics include simplified human models for Specific Absorption Rate (SAR) calculations, SAR analysis during 3T MRI scans, unintended activation of implanted medical devices during MRI, and evaluations of electric fields and SAR when tissues are near RF sources. The section concludes with the introduction of a new male model compatible with CAD, developed from the Visible Human Project.

Part 7, the final section, discusses essential methods for building detailed brain and body models. It begins with guidelines for preparing head models for simulations that combine the Boundary Element Method with the Fast Multipole Method (FMM). It then assesses the FMM’s performance in head modeling, and ends with an analytical solution that describes how a conductive object (representing the human head) responds to external electric fields.

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