Biology subjects

Maselli, A.

Publications and source records attributed to Maselli, A..

6 recordsLinked to original sources

bioRxiv2025

Scale-dependent brain age with higher-order statisticsfrom structural magnetic resonance imaging

Inferring chronological age from magnetic resonance imaging (MRI) brain data has become a valuable tool for the early detection of neurodegenerative diseases. We present a method inspired by cosmological techniques for analyzing galaxy surveys, utilizing higher-order summary statistics with multivariate two- and three-point analyses in 3D Fourier space. This method offers physiological interpretability during the inference, allowing the detection of scales where brain anatomy differs across age groups, providing insights into brain aging processes. Similarly to the evolution of cosmic structures, the brain structure also evolves naturally but displays contrasting behaviors at different scales. On larger scales, structure loss occurs with age, possibly due to ventricular expansion, while smaller scales show increased structure, likely related to decreased cortical thickness and gray/white matter volume. Using MRI data from the OASIS-3 database of 869 sessions, our method predicts chronological age with a Mean Absolute Error (MAE) of 3.1 years, while providing information as a function of scale. A posterior density estimation shows that the 1-{sigma} uncertainty for each individual varies between [~] 2 and 8 years, suggesting that, beyond sample variance, complex genetic or lifestyle-related factors may influence brain aging. We perform a twofold validation of the method. First, we apply the method to the Cam-CAN dataset, yielding a MAE of [~] 5.9 years for the age range from 18 to 88 years. Second, we apply the method to thousands of simulated MRI images generated with a state-of-the-art Latent Diffusion model. This work demonstrates the utility of interdisciplinary research, bridging cosmological methods and neuroscience. PACS87.19.lf, 87.61.Jc, 02.50.Tt, 87.19.lo

neuroscience↗

bioRxiv2025

Whole-body coarticulation reflects expertise in sport climbing

Taking sport climbing as a testbed, we explored coarticulation in naturalistic motor-behavior at the level of whole-body kinematics. Participants were instructed to execute a series of climbing routes, each composed of two initial foot-moves equal in all routes, and two subsequent hand-moves differing across routes in a set of eight possible configurations. The goal was assessing whether climbers modulate the execution of a given move depending on which moves come next in the plan. Coarticulation was assessed by training a set of classifiers and estimating how well the whole-body (or single-joint) kinematics during a given stage of the climbing execution could predict its future unfolding. Results showed that most participants engage in coarticulation, with temporal and bodily patterns that depend on expertise. Non-climbers tend to prepare the next-to-come move right before its onset and only after the end of the previous move. Rather, expert-climbers (and to a smaller extent, beginner-climbers) show early coarticulation during the execution of the previous move and engage in adjustments that involve the coordination of a larger number of joints across the body. These results demonstrate coarticulation effects in whole-body naturalistic motor behavior and as a function of expertise. Furthermore, the enhanced coarticulation found in expert-climbers provides hints for experts engaging in more refined mental processes converting abstract instructions (e.g., move the right hand to a given location) into motor simulations involving whole-body coordination. Overall, these results contribute to advancing our current knowledge of the rich interplay between cognition and motor control. NEW & NOTEWORTHYThe current study explores the way in which having formed a plan for a sequential motor task affects its execution. We showed that climbing expertise increases the extent to which participants adjust their motor execution based on the moves that follow in a planned route. These results provide evidence of coarticulation in naturalistic motor behavior and suggest enhanced skills in mentalizing forward motor control and optimal-control strategies in expert climbers.

neuroscience↗

bioRxiv2024

Eye and hand coarticulation during problem solving reveals hierarchically organized planning

During everyday activities--such as preparing a cup of coffee or traveling across cities--we often plan ahead and execute sequences of actions. However, much remains to be understood about how we plan and coordinate such sequences (e.g., eye and hand movements) to solve novel and challenging tasks, for which plans must be formed from scratch. This study investigates how participants coordinate gaze and cursor movements during problem solving tasks that involve selecting a trajectory on a grid connecting multiple targets. By focusing on the action execution phase, we aimed to probe the structure of the gaze-cursor plans that participants used to solve the tasks. Our analysis reveals three main findings. First, consistent with previous studies, participants segment the problem into sequences of gestures; within each gesture, gaze focuses on a target and remains fixed until the cursor reaches it, then shifts to the next target. Second, both gaze position--while fixating on the current target--and the kinematics of cursor movement leading up to the current target allow prediction of the next cursor movements direction, revealing coarticulation in both cursor-cursor and gaze-cursor movements. Third, and most interestingly, the position of the gaze around the current target aligns with the direction of the next saccade, revealing coarticulation between successive gaze fixations. Together, these findings show that participants break the problem into gesture sequences and plan multiple eye and cursor movements in advance to efficiently reach both the current and upcoming gesture targets. This suggests a hierarchical planning strategy, with participants planning ahead at two levels: gesture targets and cursor movements.

animal behavior and cognition↗

bioRxiv2023

Adaptive planning depth in human problem solving

We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them are not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants and planning models, we found that when facing problems that require planning to a certain number of subgoals (from 1 to 8), participants make an adaptive use of their cognitive resources - namely, they tend to select an initial plan having the minimum required depth, rather than selecting the same depth for all problems. These results support the view of problem solving as a bounded rational process, which adapts costly cognitive resources to task demands.

neuroscience↗

bioRxiv2020

Rethinking GPS Navigation: Creating Cognitive Maps Through Auditory Clues

GPS navigation is commonplace in everyday life. While it has the capacity to make our lives easier, it is often used to automate functions that were once exclusively performed by our brain. Staying mentally active is key to healthy brain aging. Therefore, is GPS navigation causing more harm than good? Here we demonstrate that traditional turn-by-turn navigation promotes passive spatial navigation and ultimately, poor spatial learning of the surrounding environment. We propose an alternative form of GPS navigation based on sensory augmentation, that has the potential to fundamentally alter the way we navigate with GPS. By implementing a 3D spatial audio system similar to an auditory compass, users are directed towards their destination without explicit directions. Rather than being led passively through verbal directions, users are encouraged to take an active role in their own spatial navigation, leading to more accurate cognitive maps of space. Technology will always play a significant role in everyday life; however, it is important that we actively engage with the world around us. By simply rethinking the way we interact with GPS navigation, we can engage users in their own spatial navigation, leading to a better spatial understanding of the explored environment.

neuroscience↗

bioRxiv2019

A Hessian-based decomposition to characterize how performance in complex motor skills depends on individual strategy and variability

In complex real-life motor skills such as unconstrained throwing, performance depends on how accurate is on average the outcome of noisy, high-dimensional, and redundant actions. What characteristics of the action distribution relate to performance and how different individuals select specific action distributions are key questions in motor control. Previous computational approaches have highlighted that variability along the directions of first order derivatives of the action-to-outcome mapping affects performance the most, that different mean actions may be associated to regions of the actions space with different sensitivity to noise, and that action covariation in addition to noise magnitude matters. However, a method to relate individual high-dimensional action distribution and performance is still missing. Here we introduce a de-composition of performance into a small set of indicators that compactly and directly characterize the key performance-related features of the distribution of high-dimensional redundant actions. Central to the method is the observation that, if performance is quantified as a mean score, the Hessian (second order derivatives) of the action-to-score function determines how the noise of the action distribution affects the average score. We can then approximate the mean score as the sum of the score of the mean action and a tolerance-variability index which depends on both Hessian and action covariance. Such index can be expressed as the product of three terms capturing noise magnitude, noise sensitivity, and alignment of the most variable and most noise sensitive directions. We apply this method to the analysis of unconstrained throwing actions by non-expert participants and show that, consistently across four different throwing targets, each participant shows a specific selection of mean action score and tolerance-variability index as well as specific selection of noise magnitude and alignment indicators. Thus, participants with different strategies may display the same performance because they can trade off suboptimal mean action for better tolerance-variability and higher action variability for better alignment with more tolerant directions in action space. Author summaryWhy do people differ in their performance of complex motor skills? In many real-life motor tasks achieving a goal requires selecting an appropriate high-dimensional action out of infinitely many goal-equivalent actions. Because of sensorimotor noise, we are unable to execute the exact same movement twice and our performance depends on how accurate we are on average. Thus, to understand why people perform differently we need to characterize how their action distribution relates to their mean task score. While better performance is often associated to smaller variability around a more accurate mean action, performance also depends on the relationship between the directions of highest variability in action space and the directions in which action variability affects the most the outcome of the action. However, characterizing such geometric relationship when actions are high dimensional is challenging. In this work we introduce a method that allows to characterize the key performance-related features of the distribution of high-dimensional actions by a small set of indicators. We can then compare such indicators in different people performing a complex task (such as unconstrained throwing) and directly characterize the most skilled ones but also identify different strategies that distinguish people with similar performance.

neuroscience↗