fMRI Brain Connectivity Differences for use in Psychiatric Diseases

    Psychiatric diseases and mental disturbances such as depression, Alzheimer’s, schizophrenia, etc are often times hard to classify or treat due to the disease affecting people individuals in different ways, and the largely unknown biological/cellular level actions taking place. While objective tests are able to diagnose a wide range of diseases from the heart to cancers, mental state is hard to properly diagnose. Psychiatric conditions are often vague when it comes to classification as many mental diseases share similar factors and symptoms.

The brain is a complex system of circuits and neural networks that can go wrong over time. Finding a more definitive way in diagnosing psychiatric diseases has been a big push as many individuals across the world suffer and often medications are not as effective due to the varying scope of certain diseases. In the article, “Brain Imaging identifies Different types of Depression”, focuses on the use of fMRI in order to develop biological markers which can be used to distinguish between different forms of depression. In using fMRI they hope to measure the strength of connections between neural circuits in the brain. Conor Liston published in Nature Medicine his research on fMRI used to measure neural circuit connections, where they identified four subtypes of depression.  This study was then furthered by a team from Weill Cornell Medicine who measured resting-state fMRI where they checked for differences in brain connectivity between depressed and healthy individuals. Participants were scanned for five minutes while lying down on a bed. They ended up with scans from 1188 individuals. They examined 258 brain areas and measured how strongly each of them correlated with other areas. They then used machine learning to find patterns within the data, which allowed them to distinguish depressed from healthy individuals based on differences in brain connectivity via fMRI.

The research done by Liston and the Cornell team correlates with the research of Gratton et al. (2019) where they explored functional/precision fMRI in order to individualize patient results which then could be used for better diagnosis and treatment. In their research, they found that FC (functional connectivity) was able to divide the brain into regions that approximate functional areas and map onto differences in task function. Both research groups ran resting-state fMRI which is useful in defining systems throughout the brain. The data collected in Gratton’s article ran task-dependent fMRI as well while Listons’ team seems to only have done resting-state, which likely led to deviations in data between the two. Gratton’s study found that while many individuals show evidence of classic network patterns, there are still clear deviations from the typical group for each individual measured. While fMRI does not directly measure nerual events but rather metabolic changes correlated with neural activity, it can be paired with pre-existing diagnostic tools, and the addition of machine learning to give a more concise connection, pattern among individuals. Pair that with the extended precision fMRI data that was collected such as that in Gratton's article can guide tonot only better diagnosis, but more specialed approaches to individuals. This means not throwing everyone into broad categories such as just depression. The personalized data could be better suited in providing better treatment whether it be certain medications, therapies, etc.

Both research groups foundstrong evidence that the use of fMRi can be used to distinguish individual brain networks among individuals which could lead to a more concise diagnosing of psychiatric diseases. In the future precision fMRI may be able to identify possible distinguishable biomarkers in these neurological diseases. Liston et als. (2017)  study was able to clearly break down depression into subgroups which would aid in better treatment of individuals falling into different forms of depression. Gratton et al. (2019) were able to also present clear individual differences in neural networks among individuals. Within Gratton’s article, they cited work done by Wang et al. who examined fMRI data of participants diagnosed with schizophrenia and bipolar disorder, which displayed FC derived from individually defined regions significantly predicted symptom levels. In Brennen et al., patients with OCD found that brain networks modeled using individually defined regions outperformed group-defined regions in predicting symptoms. The findings of the articles point that fMRI could be an effective measurement technique in better defining symptoms of psychiatric disorders and then further subtyping them to better treat individuals. 

References

Gazzaniga, M. S., Ivry, R. B., & Mangun, G. R. (2019). Cognitive neuroscience: the biology of the mind. W.W. Norton & Company.

Gratton, C., Kraus, B. T., Greene, D. J., Gordon, E. M., Laumann, T. O., Nelson, S. M., Dosenbach, N. U. F., & Petersen, S. E. (2020). Defining individual-specific functional neuroanatomy for precision psychiatry. Biological Psychiatry, 88(1), 28–39. https://doi-org.flagship.luc.edu/10.1016/j.biopsych.2019.10.026

Landhuis, E. (2017, February 21). Brain imaging identifies different types of depression. https://www.scientificamerican.com/article/brain-imaging-identifies-different-types-of-depression/.