Biology subjects

Pini, L.

Publications and source records attributed to Pini, L..

6 recordsLinked to original sources

bioRxiv2026

White-matter disconnection shapes distributed cortical spectral dynamics after stroke

Focal brain lesions are thought to induce sleep-like slow-wave activity in perilesional cortex through altered excitation-inhibition balance and structural disconnection, but whether these dynamics extend to remote yet structurally intact regions remain unclear. Here we combined source-reconstructed high-density EEG (128 channels) with structural disconnection mapping in 49 acute stroke patients and 20 age-matched controls. Cortical regions were classified as perilesional, structurally disconnected, or non-disconnected using individual lesion masks registered to normative white-matter atlases. Perilesional cortex showed increased delta and theta power and reduced beta power relative to controls. Critically, structurally disconnected regions exhibited electrophysiological changes comparable to perilesional cortex, including enhanced low-frequency activity and steeper aperiodic spectral slopes. These alterations correlated with neurological severity and multidomain behavioral impairment. Our findings demonstrate that post-stroke slow-wave activity propagates along structural disconnection pathways, providing direct electrophysiological evidence for connectional diaschisis and identifying distributed network targets for physiology-guided neuromodulation.

neuroscience↗

bioRxiv2026

Neural integrator and orchestrator communities shape spontaneous signaling in the human brain

Understanding how intrinsic brain dynamics are organized is critical for explaining human cognition and sensory processing. Theoretical frameworks propose a hierarchical architecture in which some neural systems act as orchestrators, broadcasting information across the brain, whereas others serve as integrators, transforming incoming signals. Here, we quantitatively test whether this orchestration-integration framework is embedded in intrinsic brain activity, that is, in the absence of explicit cognitive/sensorial tasks. We adopt a multivariate fractional modeling framework originally developed in financial mathematics to characterize how volatility propagates across interacting markets, thereby identifying systems that act as orchestrators or integrators. We then test whether this integrator-orchestrator axis is related to human intelligence. To this end, using 7T resting-state functional magnetic resonance imaging data from 173 healthy young adults, we model spontaneous brain fluctuations with a multivariate fractional Ornstein-Uhlenbeck process to derive directional influence indices. Consistent with our predictions, we identified a bipartite organization, stable across modeling choices. At the subcortical level, the anterior thalamus, putamen, and caudate emerged as orchestrating transmitters, whereas the posterior thalamus, globus pallidus, hippocampus, amygdala, and nucleus accumbens acted as integrating receivers. At the cortical level, attentional and sensory networks functioned as orchestrating transmitters, while higher-order cognitive networks served as integrating receivers. These findings provide support for a theoretically grounded integration-orchestration framework, demonstrating that brain signaling is organized along this axis even at rest, relevant for intelligence scores. The proposed fractional framework offers a principled tool to investigate how disruptions of this balance may contribute to brain disorders. Competing Interest StatementNone.

neuroscience↗

bioRxiv2025

Structural Connectome Dimension Shapes Brain Dynamics in Health and Disease

The structural connectome serves as the foundation for neural information signaling, playing a primary constraint on brain functionality. Yet, its influence on emergent dynamical properties is not fully understood. Generally, a key measure of a systems structural impact on dynamical phenomena is its dimension. By tracking the temporal evolution of diffusive perturbations, we estimate a scale-dependent measure of dimension of empirical connectomes. At the local scale, it is highly heterogeneous and follows a gradient from sensory-motor to high-cognitive areas. At the global scale, it encapsulates mesoscale topological information related to the balance between segregation and integration. Furthermore, by comparing connectomes from stroke patients, we find that dimension captures the local effects of lesions and, at a global level, is linked to impaired critical patterns and decreased cognitive performance. Overall, the dimension of the connectome may serve as a powerful descriptor for bridging the gap between structure and function in the human brain.

neuroscience↗

bioRxiv2025

Spontaneously emerging patterns in human motor cortex code for somatotopic specific movements

The role of spontaneous brain activity remains a key question in neuroscience. While prior work shows sensory regions preferentially replay stimulus-specific patterns (e.g., faces in fusiform face area), we investigated whether motor cortex replays reflect somatotopic organization. Using fMRI, we compared resting-state activity to task-evoked patterns during specific movements (finger, toe, tongue). Spontaneous activity in each effector-specific motor subregion (hand, foot, mouth) exhibited greater similarity to task patterns of its corresponding movement than to non-preferred ones. For instance, hand movement patterns replayed more in hand regions than elsewhere. Positive rest-task correlations emerged for preferred movements, while negative correlations characterized non-preferred ones (except toe). This structured somatotopic organization suggests spontaneous activity encodes functional specialization, not just activation strength, mirroring task-evoked representations.

neuroscience↗

bioRxiv2022

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning

The pervasive impact of Alzheimers disease on aging society represents one of the main challenges at this time. Current investigations highlight two specific misfolded proteins in its development: Amyloid-{beta} and{tau} . Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on a machine learning approach. The proposed method applies an autoregressive model, constrained by structural connectivity, to predict concentrations of Amyloid-{beta} two years after the provided baseline. In experiments, the autoregressive model generally outperformed the state-of-art models yielding the lowest average prediction error (mean-squared-error 0.0062). Moreover, we assess its effectiveness and suitability for real case scenarios, for which we provide a web service for physicians and researchers. Despite predicting amyloid pathology alone is not sufficient to clinical outcome, its prediction can be helpful to further plan therapies and other cures.

neuroscience↗

bioRxiv2022

A low dimensional cognitive-network space in Alzheimer's disease and frontotemporal dementia

Network neuroscience is a promising approach to explore cognitive processes in neurological disorders. Alzheimers disease (AD) and frontotemporal dementia (FTD) show network dysfunctions linked with cognitive deficits. Within this framework, network abnormalities between AD and FTD show both convergent and divergent patterns. However, these functional patterns are far from being established and their relevance to cognitive processes remains to be elucidated. In this study, we aimed to investigate the relationship between cognition and functional connectivity of major cognitive networks in these diseases. Twenty-three bvFTD (age: 71{+/-}10), 22 AD (age: 72{+/-}6) and 20 controls (age: 72{+/-}6) underwent cognitive evaluation and resting-state functional MRI. Principal component analysis was used to describe cognitive variance across participants. Brain network connectivity was estimated with connectome analysis. Connectivity matrices were created assessing correlations between parcels within each functional network. The following cognitive networks were considered: default mode (DMN), dorsal attention (DAN), ventral attention (VAN) and frontoparietal (FPN) networks. The relationship between cognition and connectivity was assessed using a robust convergent correlation-wise and interaction analyses. Three principal cognitive components explained more than 80% of the cognitive variance: the first component (cogPC1) loaded on memory, the second component (cogPC2) loaded on emotion and language, the third component (cogPC3) loaded on the visuo-spatial and attentional domains. Compared to HC, AD and bvFTD showed impairment in all cogPCs (p<0.002), and bvFTD scored worse than AD in cogPC2 (p=0.031). At the network level, the DMN showed a robust association in the whole group with cogPC1 and cogPC2, and the VAN with cogPC2. By contrast, DAN and FPN showed a divergent pattern between diagnosis and connectivity for cogPC2. We confirmed these results by means of a multivariate analysis (canonical correlation). These results suggest that a low-dimensional representation can account for a large variance in cognitive scores in the continuum from normal to pathological aging. Moreover, cognitive components showed both convergent and divergent patterns with connectivity across AD and bvFTD. The convergent pattern was observed across the networks primarily involved in these diseases (i.e., the DMN and VAN), while a divergent FC-cognitive pattern was mainly observed between attention/executive networks and the language/emotion cognitive component, suggesting the co-existence of compensatory and detrimental mechanisms underlying these components.

neuroscience↗