Resting State Functional MRI Patterns: A Neural Fingerprint for Human Behavior

Functional magnetic resonance imaging (fMRI) indirectly and non-invasively measures neural activity. Resting state spontaneous fMRI signals measured in the absence of any task resemble signals evoked by task performance both in topography and inter-regional functional connectivity, suggesting a close relationship between resting state patterns and behavior. However, the fundamental function of resting state spontaneous brain activity is still unknown. The present dissertation aimed to investigate the relationship between intrinsic activity and task-evoked activity to better understand the nature and function of resting state intrinsic brain activity. We measured the blood oxygen level dependent (BOLD) signal with fMRI in the human brain to analyze the multi-vertex activity patterns of intrinsic activity and compare them to those evoked by four different hand movements. In Chapter 1, we review previous evidence of an association between intrinsic and task-evoked activity. In Chapter 2, a study related to data preprocessing methods essential to compute task-rest similarity is presented. Specifically, we used two different data preprocessing pipelines: one is an unimodal pipeline, in which we included signals from T1-weighted images for structural reconstruction, and the other one is a multimodal pipeline, where signals from both T1-weighted and T2-weighted images were included for structural reconstruction. We compared both structural and functional outcomes between two pipelines and reported that functional metrics are only slightly different when using an unimodal compared to a multimodal approach, while the structural output can be significantly affected. In Chapters 3 to 5, we investigate the hypothesis that spontaneous activity patterns play a representational role and code for behaviorally related information. The main idea is that patterns of activity induced by behaviorally relevant stimuli over long periods would be represented in spontaneous activity patterns. Specifically, we investigate the relationship between resting state intrinsic activity and task-evoked activity with different regions of interest in terms of similarity using spatial correlation coefficient distributions computed between task-evoked patterns and entire resting-state BOLD frames. In Chapter 3, we show that in the motor cortex, resting state patterns are more similar to ecological movement (Grip) evoked patterns, compared to non-ecological movement (Shake) evoked patterns. In Chapter 4, we show that activity patterns related to hand movements replay at rest in frontoparietal regions of the human attention system. Interestingly, the results are more diffuse. In the dorsal attention network, resting-state patterns were more likely to match task patterns for the ecological movements than the control movement. In contrast, rest-task pattern correlation was more likely for less ecological movement in the ventral attention network. Based on the divergent effect between dorsal attention network and ventral attention network, we present a perspective shown in Chapter 6. In Chapter 5, we identify three similar coactivation patterns from rest and task and these resting-state coactivation patterns code for hand movements. In conclusion, this dissertation shows that the link between intrinsic activity and task-evoked activity is not only limited to inter-regional functional connectivity but also extends to multivoxel patterns that carry behavior-related information. These findings show that spontaneous activity in the human cortex could be a fundamental aspect of the neural framework that supports cognitive processes, facilitating the preservation and reinforcement of information representations related to stimuli and behaviors.