Neural Networking Throughout the Ages

     Our actions, decisions, and everyday behavior are guided by a slew of neural networks. Neural networks and their organization have been shown, via fMRIs, to vary across individuals. Functional MRIs work by detecting changes in blood flow in the brain. Throughout the last thirty years, fMRIs have been a great tool for psychometric assessment. With further research and development, fMRIs can offer more personable care for patients receiving neuropsychiatric care.

    In the article “Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive Daily Variation”, Gratton et. al. explores how neural networks contribute to behavior and goal directed-function. In Dr. Gratton’s lab, researchers took fMRIs while participants engaged in four separate tasks. A fMRI was also taken while the participant was not doing a task to serve as a baseline when comparing the variations. 

Gratton et al. found that the variations of neural networks were based in stable factors. These stable factors include genetics and long-term neural connections. Transient factors were not shown to contribute to neural networks on a grand scale. The variations in neural networks were also found to differ by individuals, not by tasks or sessions. There were slight differences based on tasks, and differences in neural networks based on sessions were indiscernible. Overall, differences in neural networks were recorded mostly on an individual basis. This is most likely due to the participant having more or less connectivity within each neural network when compared to the other participants.

A study that further delves into functional connectivity (FC) is detailed in the article “Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature” by Grayson et al. This study used resting state to fMRIs to show the relation between functional connectivity and structure, and how it evolved through various ages. The study found that at ages 1-2, the infants’ brains had prototypical versions of networks that are commonly found in adults. Adults have the highest functional connectivity within cingulo-opercular and somatomotor networks. The cingulo-opercular and somatomotor networks showed the most development from ages 10-26. This follows the generally accepted truth that the brain stops developing around age 25. Whereas sensorimotor systems displayed minimal change when compared from childhood age to adult age. Extrapolating this, we could infer that there is an evolutionary advantage to having a sensorimotor network develop first.  Functional connectivity developed well throughout adolescence, plateauing at the mid-twenties. These findings also reinforced the notion that network variation, both intranetwork and internetwork differences, varied by individual.

Network neuroscience is a discipline that emerged within the last decade, and needs more findings. Both of these articles report that the participant reported falling asleep or moving their head while the fMRI was being taken, which could distort the findings. Neural networks could produce many great advancements in the field of artificial intelligence, as well as studying human’s intelligence and learning. With further research, neural imaging can be a great resource for applications to network neuroscience and clinical neuropsychiatry.

 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

Grayson, D., Fair, D., (2017) Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature. NeuroImage, 15-31. https://www.sciencedirect.com/science/article/pii/S1053811917301027?casa_token=n6myTRFhNNAAAAAA:-rF2ttXANoAM-Aw9P7jjeCcCWHtqO5E36aUhmtAGs_ftOpuQCPMYGBweM0q2Aea_zW-LJVipGg