16.1: Introduction

Austin Lim, PhD (DePaul University)

Editor: Monica Javidnia, PhD (Rochester University)

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The brain can be thought of as a welloiled machine made up of hundreds of billions of moving parts, all cooperating in tandem for healthy behavior and function. There is redundancy in the way these parts are organized, allowing for occasional mishaps without any significant loss of function. But when these parts interact in unusual or atypical ways, a person may develop some psychiatric disorder. The conditions described in this section likely involve dysregulations in molecules, cells, or circuits, and are therefore complex.

As far as we know, the likelihood of developing these conditions is not exclusively determined by either genes or influence from the environment. Instead, there is probably some influence from both. That is to say, none of these conditions are 100% penetrant; none of them are dictated exclusively by genetics. Having two parents or an identical twin with the condition may indicate an elevated baseline risk over the population at large, but it is not a guarantee that the disease will manifest. Environmental triggers and other exposures may lead to a sudden onset of the condition; on the other hand, certain factors in the environment may be protective against these diseases.

One of the major challenges with understanding these brain diseases is related to the difficulty of making an accurate diagnosis - as almost everything in biology exists on a spectrum, so do these brain disorders. The symptoms of these disorders frequently overlap, adding another layer of complexity. To help establish a diagnosis, the American Psychiatric Association (APA) has put together a series of criteria for psychiatrists to diagnose these complex conditions. The guidelines are compiled in a book called the Diagnostic and Statistical Manual of Mental Disorders. They are currently on the fifth revision of the text, referred to as the DSM-5. It’s an imperfect set of criteria, but it is a start towards understanding these remarkably complicated conditions.

Figure 16.1 Environment and genetics both contribute to these diseases. Studies comparing twins can be helpful in identifying the influence of genetics.

Many of the treatments we currently have for neuropsychiatric disorders aren’t always effective. Our ability to treat these conditions depends on our understanding of the disease. The better we understand the causes of these conditions, the wider variety of new therapies we can test. Therapeutic strategies for these conditions are often first tested using cells and non-human animal models, but these have shortcomings. Most of the time, animal models of disease incompletely mimic the symptoms of the disease. Animal research concerns itself with three different forms of validity.

Figure 16.2 The DSM-5 is the manual that is used by psychiatrists to diagnose various psychiatric conditions.

1. Face validity. If an animal model of a disease looks similar to the human condition, whether behaviorally or in physical appearance, we say that the model has good face validity. The animal exhibits the same set of symptoms that you would see in a human affected by the same condition, such as a rat model of post-traumatic stress disorder (PTSD) where the rat is exposed to a predator. Future exposure to predatorassociated cues causes the rat to exhibit anxiety and avoidance, which are symptoms of human PTSD. In this case, the model has good face validity.

2. Construct validity. Sometimes, an animal model of a disease starts with the same pathological changes in the brain that are observed in human patients. We say that these models have good construct validity. The risk of developing Huntington’s disease (Chapter 10) in humans is associated with the number of poly-glutamine repeats in the Huntingtin protein. Developing a genetically modified animal model that has several poly-glutamine repeats is an example of a model with good construct validity. Because humans and non-humans are very different animals, having the same origin of disease does not always produce the disease symptoms.

3. Predictive validity. An animal model has good predictive validity if the animal model can be used to predict whether a therapy would be effective in treating humans with that same condition. For example, if there were a genetic mouse model that showed symptoms of depression, and an experimental antidepressant reverses depression in both the mouse and humans with depression, the model would be said to have good predictive validity.

Unfortunately, we don’t have any animal models that reproduce the symptoms of the most complex human conditions - it is almost impossible to create a mouse model of dissociative identity disorder or dyslexia, and even if we could, scientists would struggle to quantify the behaviors that we use as diagnostic criteria, which are too subtle to be observed or quantified in non-humans. And for the animal models that we do have, they are often imperfect or incomplete, modeling only some of the deficits seen in humans. Furthermore, most human diseases have many symptoms, and only a few can be assessed with behavioral tests. We can only study disorders of the brain that have a clearly and easily quantifiable behavioral component.

Figure 16.3 A predator exposure paradigm has strong face validity because it causes a mouse to become anxious, just like a human when they are exposed to a predator.