Hands are things with which we act

How Spontaneous Brain Activity Encodes the Observation of Grasping Movements

Perciballi C, Pini L, Sili D., El Rassi Y., Zhang L,  Handjaras G, Giove F, Ricciardi E, Corbetta M, Betti V.

Neuroimage 2025, in press

Abstract: Spontaneous brain activity forms correlated networks resembling task-evoked activation patterns, yet its functional relevance remains debated. The representational hypothesis suggests that resting-state networks (RSNs) encode frequent behaviors, but whether these representations are motor-based or cognitive is unclear. Here, we used fMRI to examine RSNs activity during the observation of reach-to-grasp movements with either regular (common) or perturbed (uncommon) kinematics. We found that the dorsal attention network (DAN) exhibited greater similarity between rest and task patterns for common movements, whereas sensory networks showed no significant effects. While DAN is classically associated with attention mechanisms, these results suggest that it may also contribute to tracking the location or motion of the hand. Furthermore, uncommon movements elicited stronger activation in parietal and premotor areas, likely reflecting adaptive updating of internal models. Our findings support the role of spontaneous brain activity in maintaining cognitive representations of frequent behaviors, optimizing motor planning and perception.

A visual representation of the hand in the resting somatomotor regions of the human brain

El Rassi Y. Handjaras G. Perciballi C. Leo A. Papale P. Corbetta M. Ricciardi E.  Betti V.

Sci Rep doi: 10.1038/s41598-024-69248-z.

Abstract: Hand visibility affects motor control, perception, and attention, as visual information is integrated into an internal model of somatomotor control. Spontaneous brain activity, i.e., at rest, in the absence of an active task, is correlated among somatomotor regions that are jointly activated during motor tasks. Recent studies suggest that spontaneous activity patterns not only replay task activation patterns but also maintain a model of the body's and environment's statistical regularities (priors), which may be used to predict upcoming behavior. Here, we test whether spontaneous activity in the human somatomotor cortex as measured using fMRI is modulated by visual stimuli that display hands vs. non-hand stimuli and by the use/action they represent. A multivariate pattern analysis was performed to examine the similarity between spontaneous activity patterns and task-evoked patterns to the presentation of natural hands, robot hands, gloves, or control stimuli (food). In the left somatomotor cortex, we observed a stronger (multivoxel) spatial correlation between resting state activity and natural hand picture patterns compared to other stimuli. No task-rest similarity was found in the visual cortex. Spontaneous activity patterns in somatomotor brain regions code for the visual representation of human hands and their use.

Investigating the impact of the regularization parameter on EEG resting-state source reconstruction and functional connectivity using real and simulated data

Leone F,  Caporali A, Pascarella A, Perciballi C, Maddaluno O, Basti A,  Belardinelli P, Marzetti L, Di Lorenzo G, Betti V.

Neuroimage 2024 doi: 10.1016/j.neuroimage.2024.120896.

Abstract: Accurate EEG source localization is crucial for mapping resting-state network dynamics and it plays a key role in estimating source-level functional connectivity. However, EEG source estimation techniques encounter numerous methodological challenges, with a key one being the selection of the regularization parameter in minimum norm estimation. This choice is particularly intricate because the optimal amount of regularization for EEG source estimation may not align with the requirements of EEG connectivity analysis, highlighting a nuanced trade-off. In this study, we employed a methodological approach to determine the optimal regularization coefficient that yields the most effective reconstruction outcomes across all simulations involving varying signal-to-noise ratios for synthetic EEG signals. To this aim, we considered three resting state networks: the Motor Network, the Visual Network, and the Dorsal Attention Network. The performance was assessed using three metrics, at different regularization parameters: the Region Localization Error, source extension, and source fragmentation. The results were validated using real functional connectivity data. We show that the best estimate of functional connectivity is obtained using 10-2, while 10-1has to be preferred when source localization only is at target.

Rewiring the evolution of the human hand: How the embodiment of a virtual bionic tool improves behavior

Marucci M, Maddaluno O, Ryan CP, Perciballi P, Vasta S, Ciotti S, Moscatelli A, Betti V

iScience 2024 doi: 10.1016/j.isci.2024.109937

Abstract: Humans are the most versatile tool users among animals. Accordingly, our manual skills evolved alongside the shape of the hand. In the future, further evolution may take place: humans may merge with their tools, and technology may integrate into our biology in a way that blurs the line between the two. So, the question is whether humans can embody a bionic tool (i.e., experience it as part of their body) and thus if this would affect behavior. We investigated in virtual reality how the substitution of the hand with a virtual grafting of an end-effector, either non-naturalistic (a bionic tool) or naturalistic (a hand), impacts embodiment and behavior. Across four experiments, we show that the virtual grafting of a bionic tool elicits a sense of embodiment similar to or even stronger than its natural counterpart. In conclusion, the natural usage of bionic tools can rewire the evolution of human behavior.

Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity

Maddaluno O. Della Penna S. Pizzuti A. Spezialetti M. Corbetta M. de Pasquale F. Betti V

J  Neurosci 2024 doi: 10.1523/JNEUROSCI.1766-23.2024.

Abstract: In everyday activities, humans move alike to manipulate objects. Prior works suggest that hand movements are built by a limited set of basic building blocks consisting of a set of common postures. However, how the low dimensionality of hand movements supports the adaptability and flexibility of natural behavior is unknown. Through a sensorized glove, we collected kinematics data from thirty-six participants preparing and having breakfast in naturalistic conditions. By means of an unbiased analysis, we identified a repertoire of hand states. Then, we tracked their transitions over time. We found that manual behavior can be described in space through a complex organization of basic configurations. These, even in an unconstrained experiment, recurred across subjects. A specific temporal structure, highly consistent within the sample, seems to integrate such identified hand shapes to realize skilled movements. These findings suggest that the simplification of the motor commands unravels in the temporal dimension more than in the spatial one.

The spatio-temporal architecture of everyday manual behavior.

Sili D, De Giorgi C, Pizzuti A, Spezialetti M, de Pasquale F, Betti V.

Sci Rep. 2023 Jun 9;13(1):9451. 

Abstract: In everyday activities, humans move alike to manipulate objects. Prior works suggest that hand movements are built by a limited set of basic building blocks consisting of a set of common postures. However, how the low dimensionality of hand movements supports the adaptability and flexibility of natural behavior is unknown. Through a sensorized glove, we collected kinematics data from thirty-six participants preparing and having breakfast in naturalistic conditions. By means of an unbiased analysis, we identified a repertoire of hand states. Then, we tracked their transitions over time. We found that manual behavior can be described in space through a complex organization of basic configurations. These, even in an unconstrained experiment, recurred across subjects. A specific temporal structure, highly consistent within the sample, seems to integrate such identified hand shapes to realize skilled movements. These findings suggest that the simplification of the motor commands unravels in the temporal dimension more than in the spatial one.

Spontaneous Beta Band Rhythms in the Predictive Coding of Natural Stimuli.

Betti V, Della Penna S, de Pasquale F, Corbetta M.

Neuroscientist. 2021 Apr;27(2):184-201. 

Abstract: The regularity of the physical world and the biomechanics of the human body movements generate distributions of highly probable states that are internalized by the brain in the course of a lifetime. In Bayesian terms, the brain exploits prior knowledge, especially under conditions when sensory input is unavailable or uncertain, to predictively anticipate the most likely outcome of upcoming stimuli and movements. These internal models, formed during development, yet still malleable in adults, continuously adapt through the learning of novel stimuli and movements.Traditionally, neural beta (β) oscillations are considered essential for maintaining sensorimotor and cognitive representations, and for temporal coding of expectations. However, recent findings show that fluctuations of β band power in the resting state strongly correlate between cortical association regions. Moreover, central (hub) regions form strong interactions over time with different brain regions/networks (dynamic core). β band centrality fluctuations of regions of the dynamic core predict global efficiency peaks suggesting a mechanism for network integration. Furthermore, this temporal architecture is surprisingly stable, both in topology and dynamics, during the observation of ecological natural visual scenes, whereas synthetic temporally scrambled stimuli modify it. We propose that spontaneous β rhythms may function as a long-term "prior" of frequent environmental stimuli and behaviors.