Encoding dexterity in the human brain

Introduction: The hand plays a pivotal role in human beings' everyday life: we use our hands to manipulate objects andinteract with the surrounding environment. Manual skills emerge during infancy and refine during development[1], however individuals differ in their level of manual ability (i.e., manual dexterity). One feature of a proficient motor behavior is a clear pattern of flexibility in the motor schemas – that ensures a good performance –parallel with a certain degree of stability that embeds learned behaviors and motor memories. It is well knownthat training and learning processes induce changes in the brain [2-4]. These changes are evident even in thespontaneous activity [5] (i.e., brain activity that occur without any external input). An open question remains onhow interindividual motor variability is encoded in the large-scale organization of the resting brain to ensure theflexibility of motor behavior. Network segregation/integration mechanisms support behavioral performance [6].However, whether the topological organization of the brain, network modularity and nodal centrality vary withmanual dexterity levels it is still unknown. Methods: We analyzed data from the Human Connectome Project collected from 51 participants (age range 21-35 yo)while they performed a motor task (i.e., a finger tapping) or during visual fixation. We computed theleakage-corrected band-limited power [7,8] correlation across 164 node regions - parcelled into 10 networks -in α (6.3-16.5 Hz), low β (12.5-29 Hz), and high β band (22.5-39 Hz). We then used a k-means algorithm totest, with a data driven approach, if participants have different connectivity profiles based on their level ofdexterity. We computed Louvain modularity and participation index as measure of centrality. We used the Nine-hole peg test scores as an index of finger dexterity [9]. Results: First, our results show that finger tapping decreases connectivity, but in alpha the decrease is smaller than inthe beta bands (p<0.001). Moreover, in alpha, brain topology reorganization involves fewer links, especially inthe Motor Network. Second, we demonstrate how the clustering algorithm splits participants in 2 groups withdistinct connectivity profiles: a group exhibits an overall decrease of functional connectivity (FC) aftertask performance; the other group shows a greater stability of FC between rest and task, especially in theMotor Network and in the Dorsal Attention Network, with an increase of FC in other networks. Notably, the 2groups (from now on high and low performers) differed in terms of manual dexterity. Thus, the lack of capacityof reorganization seems to be dysfunctional in terms of performance. Third, the 2 groups exhibit different segregation/integration mechanisms. Dexterous individuals exhibit higher modularity during finger tapping thanduring rest in parallel with a decrease of nodal centrality – especially in the Motor Network and ControlNetwork. Conversely, modularity decreases in low performers while nodal centrality increases. This oppositemodulation is significant in the Motor Network. Conclusions: The individual level of skills leads to different pattern of FC reorganization. To reach an efficient behavior, wemust have a specific balance between stability/flexibility of network communication. Indeed, during the task,high performers efficiently reorganize their whole-brain topology; while low performers have a stable,dysfunctional connectivity pattern, especially in the task-evoked networks that prevent a good performance. Crucially, this scheme of flexibility/stability is observable only in the alpha band that is thus a clear marker ofmanual dexterity. Our hypothesis is that in the spontaneous activity is coded a scaffold of connectivity builtthrough experience, but this scaffold must be flexible enough to reorganize itself to enhance and supportperformance. Otherwise, a stability of the connectivity pattern turns out to be dysfunctional in terms of behavior.