Structure-function relationship in the human brain

点击翻译按钮获取中文摘要

Networks of neurons in the cerebral cortex are continuously active during purposeful behavior, as well as during rest and sleep. Levels of activity are modulated in time by the internal dynamics of each neuronal population and by signals received from cortical, subcortical, and peripheral centres of the nervous system. In recent years, there has been intense interest in the spatial patterns of correlated activity as measured by functional magnetic resonance imaging (fMRI), because these patterns are believed to reflect the spatial distribution of interaction between neuronal populations. While some functional networks are commonly detected when participants are asked to perform a task, other networks are observed when no participation is required (resting state). It has been recently suggested that these functional networks spatial patterns can be supported by the structural fiber wiring of the brain. However, the nature of this structure-function relationship is only just beginning to be revealed. In recent years some authors have begun investigating the possibility of inferring functional correlation patterns from structural connectivity by building structural connectivity maps (tractography) and extracting the couplings to be used in a computational model of the large-scale dynamics of the cerebral cortex. Subsequently, from such dynamics blood oxygen level dependent (BOLD) signals can be simulated and quantitatively compared with empirical observations from fMRI. In my research I will develop large-scale dynamics computational models to investigate the structure-function relationship in the human brain. My approach will take into account the possibility that dynamics are different in different parts of the cortex, for example to be characterized by lower or higher frequency, and could be different for different brain states or conditions. In order to assess different dynamics, I will use metabolic activity as measured by 18F-fluorodeoxyglucose positron emission tomography during rest or the power of the EEG signal spontaneous activity. I hypothesize that by introducing different dynamics for different cortex patches a better prediction of functional from structural connectivity will be obtained in healthy awake subjects and will be substantially improved in altered states of consciousness as for example induced by sleep or anesthesia. A good prediction of the empirical functional connectivity does not depend only on the model used for the simulation but also on the analysis performed to isolate in the measured BOLD signal the neuronal contribution from other spurious sources such heart beating or respiration. I will then investigate different statistical decomposition to test which one can more efficiently break down the resting brain to better isolate the neuronal component. Finally, I will employ a recently developed technique, which permits us to acquire simultaneously quantifiable perfusion images using arterial spin labeling (ASL) contrast and BOLD signals. ASL has been proven to give an absolute measure of cerebral blood flow and I hypothesize that a simultaneous acquisition will help disentangle the neuronal attribution from other possible physiological origins of the BOLD signal. In conclusion I believe my research will importantly contribute to better understand the mechanisms at the base of the synchronization patterns we observe in a resting brain. And studying how the structural wiring together with the metabolic supply can support brain information flow will finally permit me to derive the relevant features that can make any networking efficient as the brain is. For this reason I believe that other fields from neuroscience, like social science or economy, will take full advantage from my results.