From Rest to Action: Functional Connectivity Architecture of the Human Hand Motor Network

Skillful hand motor control relies on complex interactions within a distributed brain network. Over the past two decades, neuroimaging (fMRI) and neurostimulation (TMS) techniques have greatly advanced our understanding of the functional contributions of individual regions. Yet, a central open question in motor neuroscience concerns the dynamic interactions among these regions: while their local roles are well characterized, their network-level organization and functional integration remain less understood. To date, most studies have examined correlational methods (e.g., fMRI, MVPA) or causal approaches (e.g., TMS) in isolation. Each offers valuable but incomplete insights, underscoring the need for integrative approaches. In Study 1, we combined offline TMS with resting-state fMRI (rs-fMRI) to investigate stimulation-induced changes in functional connectivity of the human anterior intraparietal area (hAIP), a key parietal hub for hand motor control. Participants underwent rs-fMRI before and after continuous theta-burst stimulation (cTBS) of hAIP. Univariate analyses revealed increased connectivity between hAIP and SMA, and between SMA and M1. Multivariate analyses further identified widespread changes across fronto-parietal pathways, the ventral stream, and the cerebellum. These findings provide novel evidence that hAIP is embedded in a distributed network supporting hand motor control, mirroring — but not fully replicating — the connectivity architecture described in non-human primates. In Study 2, we applied connectome-based predictive modeling (CPM) to rs-fMRI data from the Human Connectome Project to test whether intrinsic connectivity predicts individual differences in hand motor performance. We identified a “core” hand motor network whose connectivity predicted both task-specific measures (dexterity and strength) and generalized across tasks, effectors, and even to an independent dataset with different behavioral metrics. Crucially, the predictive power of this model was selectively disrupted following TMS to inferior parietal cortex, demonstrating that the network encodes behaviorally relevant information and is sensitive to perturbation. Together, these studies show that spontaneous brain activity encodes both low-level features and higher-order, task-independent aspects of hand motor control. By linking intrinsic connectivity patterns to motor behavior, and demonstrating their causal relevance, this thesis provides new insights into the organization and flexibility of the human hand motor network. Beyond its theoretical contributions, this work highlights the potential of predictive modeling and multimodal approaches for characterizing motor dysfunction in clinical populations.