How Knowledge of Functional Brain Networks Can Advance Diagnosis and Treatment of Neurological Disorders

Every day, millions of Americans suffer from neurodegenerative diseases, from which the effects are likely to be irreversible and fatal. Diseases including Alzheimer’s, Multiple Sclerosis, and Parkinson’s are just a few of the well-known diseases that will inevitably lead to debilitating symptoms (Huizen). Fortunately, current advancements in medicine and technology allow those suffering from incurable neurological diseases some hope since current knowledge of neuroscience and cognition can give rise to new potential treatments. 

An example of the advancing knowledge regarding the nervous system can be observed in the study conducted by Caterina Gratton et al. Dr. Gratton provides an in-depth study of how to determine the varying functional brain networks in humans. To do this, she used an fMRI to measure brain activity that correlated with specific behaviors. One of the most significant findings as a result of this study is that “Functional networks are dominated by common organizational principles and stable individual features . . . they are suited to measuring stable individual characteristics, suggesting utility in personalized medicine” (Gratton et. al). 

If a patient with a neurological disorder were to receive a brain scan, there are external factors that could cause significant alterations during their examination. Examples of such external factors can include the patient’s comfort, mood, or even behavioral task before or during the scan (Gratton et. al). In any environment, the patient may likely feel a sense of discomfort, unease, or concern during the test. These are the unstable traits that will show up in the scan, and they are irrelevant in terms of significance to the disease status and the patient’s personality. For the purpose of avoiding alterations to the scan as a result of these unstable traits, Gratton proposes that using an fMRI to scan functional networks will indicate stable traits that are relevant in determining the status of the patient’s disease without the presence of any unstable traits that come from their thoughts, mood, etc. After conducting the study using ten individuals and ten fMRI sessions, the results were consistent with Gratton’s proposition. The fMRI network variance was due to individual differences rather than the differences in their given task or their unstable traits. More importantly, the results indicated that the functional networks mainly depicted the stable traits of each individual, with only slight changes that came from doing different tasks (Gratton et. al). 

Ultimately, Gratton’s method of using an fMRI to measure network variance appears to be beneficial for determining a participant’s individual differences, personality, and disease status, especially since her results indicated that the functional networks were largely stable with minor alterations from the given task. These findings can have a significant impact on the treatment and diagnosis of patients with neurological disorders since an fMRI scan can pinpoint individual differences across the brain networks and eliminate the possibility of the irrelevant unstable traits that can alter the scan. If this method is used, patients may receive a more personalized treatment rather than a universal one.