Une nouvelle approche pour l’identification des états dynamiques de la parcellisation fonctionnelle cérébrale individuelle

Brain parcellations are applied in neuroimaging to help researchers reduce the high dimen- sionality of the functional MRI data. The main objective is a better understanding of the brain functional organization in both healthy subjects and subjects having neurological dis- orders, including Alzheimer disease. Despite the flurry of previous parcellation approaches, the performance measures still need improvement to generate reliable parcellations even with long acquisitions. That is, a higher reproducibility that allows researchers to replicate par- cellations and compare their studies. It is also important to minimize the information loss between the compressed data and the raw data to accurately represent the organization of an individual brain. In this thesis, I developed a new approach to parcellate the brain into distinct spatial reconfigurations called “dynamic states of parcellations”. I used a simple cluster aggregation method DYPAC1.0 of seed based parcels over multiple time windows. I hypothesized this new way to formalize the parcellation problem will improve performance measures over static parcellations. The first chapter of this document is a general context introduction to the human brain large scale networks. I also show the importance of par- cellations for a better understanding of the human brain using functional connectomes in order to predict patterns of disease progression. Then, I explain why the brain parcellation problem is hard and the different open research questions associated with this field. My research contributions are subdivided into two papers. The second and the third chapters are dedicated to the first main paper and its supplementary published in Network Neuro- science Journal. The fourth chapter represents the second paper under preparation. The fifth chapter concludes my contributions and its implications in the neuroimaging field, along with open research directions. In a nutshell, the main finding of this work is the existence of distinct spatial reconfigurations throughout the brain with near perfect reproducibility scores across test-retest data (up to .9 Pearson correlation coefficient). A simple and scalable clus- ter aggregation algorithm called DYPAC 1.0 is explained to identify these reconfigurations or “dynamic states of parcellations” for specific seed subnetworks (second chapter). The analysis of these states showed the existence of a richer repertoire of “dynamic states” in the case of heteromodal cortices (e.g., posterior cingulate cortex and the dorsal anterior cingulate cortex) compared to unimodal cortices (e.g., visual cortex). Also, the reproducibility analysis results showed that DYPAC 1.0 has better reproducibility results (in terms of Pearson corre- lation) compared to static parcels (second chapter). Several analyses demonstrate DYPAC 1.0 is robust to the choice of its parameters (third chapter). These findings and the scalabil- ity of DYPAC 1.0 motivated a full brain level analysis. I present DYPAC 2.0 as the full brain level approach to parcellate the brain into “dynamic states of parcellations”. Distinct and overlapping spatial reconfigurations or “dynamic states” are identified for different regions throughout the brain (fourth chapter). These states have promising compression scores that show low information loss between the reduced state stability maps and the original data throughout the cerebral cortices, i.e. up to only 20% loss in explained variance. This thesis thus presents new contributions in the functional parcellation field that may impact how researchers model the rich and dynamic interactions between brain networks in health and disease.

Keyphrases: Functional magnetic resonance imaging, Spatial analysis, Subject specific regions, Dynamic states, Whole brain parcellation