Methods and models for brain connectivity assessment across levels of consciousness

The human brain is one of the most complex and fascinating systems in nature.
In the last decades, two events have boosted the investigation of its
functional and structural properties. Firstly, the emergence of novel noninvasive
neuroimaging modalities, which helped improving the spatial and
temporal resolution of the data collected from in vivo human brains. Secondly,
the development of advanced mathematical tools in network science
and graph theory, which has recently translated into modeling the human
brain as a network, giving rise to the area of research so called Brain Connectivity
or Connectomics.
In brain network models, nodes correspond to gray-matter regions (based
on functional or structural, atlas-based parcellations that constitute a partition),
while links or edges correspond either to structural connections as
modeled based on white matter fiber-tracts or to the functional coupling
between brain regions by computing statistical dependencies between measured
brain activity from different nodes.
Indeed, the network approach for studying the brain has several advantages:
1) it eases the study of collective behaviors and interactions between
regions; 2) allows to map and study quantitative properties of its anatomical
pathways; 3) gives measures to quantify integration and segregation of information
processes in the brain, and the flow (i.e. the interacting dynamics)
between different cortical and sub-cortical regions.
The main contribution of my PhD work was indeed to develop and implement
new models and methods for brain connectivity assessment in the human
brain, having as primary application the analysis of neuroimaging data
coming from subjects at different levels of consciousness. I have here applied
these methods to investigate changes in levels of consciousness, from normal
wakefulness (healthy human brains) or drug-induced unconsciousness (i.e.
anesthesia) to pathological (i.e. patients with disorders of consciousness).