The problems with our current diagnostic system
The current diagnostic system for mental disorders is documented in the DSMV, the diagnostic and statistical manual for researchers and clinicians. Currently, our diagnostic system for mental illnesses is based on symptom tracking. To receive a diagnosis, you must present with a specific set of symptoms, and clinicians will use these sets of symptoms to match you with a diagnosis. For example, to have a depression diagnosis, you need 5 out of 9 symptoms. This means that two people who have depression may only share 1 symptom. For researchers and clinicians, this makes developing a standardized treatment protocol, or doing research about depression becomes very challenging when as little as 1 symptom is shared between clients. Our available diagnostic tool is very imprecise and does not allow for precise treatment, or for the practice of "precision medicine."
Because our diagnostic tool is based on symptom tracking, our treatment protocols are organized around symptom relief. This can still provide life-changing support to patients however symptom relief often doesn't often lead to full recovery. We need to find a deeper cause to diagnose from so that we can more effectively treat these disorders.
One example of how diagnostic improvement transformed medical care is found in Cancer treatment. Cancer is now only partially diagnosed based on location (such as breast, bone, or brain cancer), it is now also diagnosed based on the molecular structure of the cancer cells. This has completely transformed cancer treatment and allows doctors to treat cancer with greater precision and specificity.
There was hope, in mental health, for gene therapy to be the next great diagnostic tool. This was especially true for diagnosing bipolar and schizophrenia. Currently, these disorders have more heritability than cancer and are easier to identify in DNA tests than most physical illnesses. The heritability of bipolar and schizophrenia has surpassed the heritability of cancer, diabetes, and hypertension.
The problem with genomics as a reliable diagnostic tool is that it is so complicated. It’s not as if it is 1 genome variation that leads to bipolar disorder - it is 200 variations. Each of the variations are subtle and none alone can be considered causal. The variations are considered increased risk factors at best. Because it is so complex, making a family tree is just as useful as taking a DNA test.
We've also explored brain imaging as a path to more precise diagnostics.
Currently, mental health disorders do not have any observable brain lesions. We can't take an image of a brain and see if someone has bipolar or anxiety. However, there is potential for more research on "circuit" abnormalities as related to mental health disorders.
Our brain is a complex network with 128 billion neurons connected as a single, massive, and flexible structure. As described by Lisa Barret, in 7 1/2 lessons about the brain, these neurons are grouped into clusters, and the clusters serve as "hubs" of communication across the brain. These neurons and their communication pathways, which we often call "circuits", are changing instantaneously and continuously. If one of these clusters "goes down" problems can occur that are associated with depression, schizophrenia, dyslexia, chronic pain, dementia, Parkinson's, and many other disorders.
One such circuit is the default mode network. This is a communication network in the midline of the brain that is not obviously connected anatomically yet seems to synchronize, especially when the brain is not engaged in a task. Some people call it the daydreaming circuit. Individual variation in the default mode network suggests it may be one of many functional circuits important in mental illness. Could the circuit approach yield more precise diagnostic categories? The current data on this theory isn’t consistent or specific - however, it is worth exploring.
Additionally, fMRI's provide not just imaging of structure but also activity and connection info on the brain. Currently, there are hopes that it could be useful in diagnosing depression. Through fMRI' studies we have identified different "sub-types" of depression, and have begun to notice differences in brain networks for other mood disorders. There is a possibility that with time and more research, this diagnostic method could be useful in a clinical setting.
Rebelling against labels
Many people these days, are resistant to labels and are anti-mental health diagnostics. Because our current diagnostic system is so poor, mental health diagnostics as a whole now have a bad rap. In part, this is because mental illness starts at a young age - most people experience the first symptoms of mental illness before the age of 25. During the teen and young adult years, one's ego or identity has not fully formed and so there is easy identity confusion with diagnostic labels. Meaning, that the diagnosis can easily become conflated with the patient's identity. Rather than “I have” it becomes “I am”. This is a normal confusion for teens and young adults to make and rebellion against those labels is a common response for patients of this age.
This rebellious sentiment paired with increasing distrust in mental health treatment options (such as medication), has created a belief that labeling human suffering pathologizes normal variation and medicalizes the human experience. Of course, just because we use a medical approach to define the problem does not mean we won't use a social or relational approach to solving it. These negative attitudes against labels and categories for mental health may be a larger challenge against progress in the field of mental health than biology itself.
Why we need a diagnostic system
Without an accurate classification mechanism, we cannot establish reliability and validity in the field of mental health. We are currently unable to do accurate research, give exact treatment protocols or establish reliable, high-quality care for clients. Because of this, it is easier for insurance companies to not make mental health treatment a priority for coverage and we end up paying for avoidable long-term disabilities and putting the burden of care on families and communities. Creating a deeper and more exact diagnostic tool would positively transform the field.