Specifically, in an effort lead by Tianwen Chen, Ph. D., the Stanford team has develop novel tools to identify the functional organization of brain networks in the human brain, specifically they have addressed the novel trend in characterizing the spatiotemporal organization of the saliency network.
The salience network, anchored in the anterior insula and the dorsal anterior cingulate cortex, plays a crucial role in this process through rapid detection of goal-relevant events and facilitation of access to appropriate cognitive resources. In their contribution (see publications), the Stanford team leveraged the subsecond resolution of large multisession fMRI datasets from the Human Connectome Project and applied novel graph-theoretical techniques to investigate the dynamic spatiotemporal organization of the saliency network. Specifically, they show that the large-scale brain dynamics of the saliency network are characterized by several distinctive and robust properties.
This novel development on functional MRI analysis is at the core of the main goal of this grant: the structure-function relationship in the brain. Moreover, this method has been applied to the HCP database, which is the common healthy subject database of the LargeBrainNets associated team.
On one hand Ph.D. student R. Fick along with R. Deriche and D. Wassermann (French PI) developed novel pre-processing techniques to quantify structural connectivity (SC) from multi-shell diffusion MRI. SC measures based on physiological models of the axons, which yield an inverse relationship between conductivity speed and axonal caliber. The measurement of axonal caliber in the whole human brain in-vivo is now possible due to multi-shell dMRI such as those of HCP. In developing novel SC estimation techniques based on multi-shell dMRI to determine the presence of a connection and its strength using axonal caliber, we will narrow the gap between SC and functional connectivity. The main outcome of this research, a method dubbed MAPL, has been recently published in the journal Neuroimage (see publications). On the other hand Ph.D. student G. Gallardo and D. Wassermann (French PI) have developed a novel method for the parcellation of the cortex of the brain as well as of subcortical structures. We proposed a parsimonious model for the extrinsic connectivity and an efficient parcellation technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with structural and functional parcellations in the literature. In particular, the motor and sensory cortexes are subdivided in agreement with the human homunculus of Penfield. We illustrated this by comparing our resulting parcels with the motor strip mapping included in the HCP data. This work has been presented in two conferences (see publications). The journal version has been submitted. This work has been given a student award at the Human Brain Mapping conference.
These methods are the cornerstone of the relationship between structure and function in the brain, which is the core goal of this grant. Moreover, both tasks have been applied to the HCP database, which is the common healthy subject database of the LargeBrainNets associated team.
Currently both groups are putting together their functional and diffusion MRI analysis techniques to analyze the anatomo-functional nature of brain function and behaviour. For this, the LargeBrainNets Stanford-Inria team is working towards elucidating the anatomical substrate of specific functional networks and cognitive processes. Conference abstracts issued from this collaboration have been submitted to the 2017 Organization for Human Brain Mapping Meeting.
Large Brain Nets Associate Team