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Characterising In Vivo Brain Function Following Mild Traumatic Brain Injury



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Kelleher-Unger, Isaac 


Traumatic brain injury (TBI) is the most complex disease in the most complex organ and a public health emergency. Sometimes called a silent epidemic, there are at least 50 million TBIs each year globally. This incidence translates to half the world’s population having at least one TBI in their lifetime, and evidence suggests that this number is increasing.

At least 70% of TBIs are Mild traumatic brain injuries (mTBIs), yet, despite this incidence, these patients remain understudied on the assumption that they make near-complete recoveries with little to no mortality. However, literature shows that 80% of patients report at least one post-traumatic symptom one-year after injury. Ultimately, research into mTBI aims to improve outcomes for this group of patients — the principal and necessary next steps toward achieving this aim are to characterise mTBI more thoroughly and make diagnosis/prognosis more precise. Notably, how the brain functions — in terms of the connections it makes and the way it processes information — after mTBI is yet to be fully understood. Hence, in this thesis, I aimed to answer two questions: first, can Functional magnetic resonance imaging (fMRI), as an in vivo functional imaging technique, help to more thoroughly characterise brain function after an injury? And, second: can in vivo functional imaging make mTBI diagnosis more precise?

I adopted four approaches to answer these questions. In my first chapter, I highlight that there are no macroscopic structural alterations following mTBI. However, individuals with brain injuries showed elevated markers of neuronal/glial damage. These two observations underscore that the absence of macroscopic structural pathology is not evidence of a lack of injury, and highlight the need to characterise mTBI beyond current conventions. In particular, in the absence of macroscopic structural injury, mTBI might be better understood in the functional domain.

To this end, I first characterised the functional phenotype of the brain following injury. Here, I quantified global connectivity and global complexity of the brain and found that following injury, the brain becomes hyperconnected and hypocomplex. Moreover, these two measures negatively correlated with each other.

Next, I characterised the dynamics of brain connectivity using hidden Markov modelling. Here I found a shift in dynamics: patients with mTBI spend less time in a state dominated by connections between subcortical-cortical regions, shifting to a state characterised by cortico-cortical connections. To establish the importance of this, I used machine learning techniques to investigate if the shift in brain dynamics could predict long-term symptoms, finding that altered cerebral dynamics could achieve a reasonable level of accuracy in predicting long-term symptoms following mTBI .

Finally, I investigated how specific functional connections are affected in mTBI. Particularly, given the evidence that sulci are a concentrated location of pathology following mTBI, I investigated how an injury affects sulcal functional connectivity. In this analysis, I found altered interhemispheric sulcal connectivity following injury. However, sulcal and gyral functional connectivity achieved similar accuracies for classifying patients with mTBI from healthy controls, supporting the idea that the effects of mTBI are not localised, but distributed. More importantly, using a novel explanatory artificial intelligence methodology, I showed how sulcal connectivity could personalise mTBI diagnosis in a proof-of-concept manner.

In answering my first research question, my results have shown that macroscopic function can help characterise the injured brain, advocating that no TBI is mild and that conceptualising mTBI as a disorder of functional connections can offer new insights. In answering my second research question, my results show evidence that mTBI is a heterogeneous disease. By highlighting that different connections are important for different individuals, I have shown that mTBI needs precision diagnostics and provided a starting point for future research to continue this pursuit.





Stamatakis, Emmanuel


Brain Injury, fMRI, MRI, Neuroscience


Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge