Biology subjects

Zorzi, M.

Publications and source records attributed to Zorzi, M..

5 recordsLinked to original sources

bioRxiv2026

Towards a scalable effective connectivity estimation in fMRI

Characterizing directed connections between brain regions is fundamental for understanding the brains network organization and how it supports cognitive processes. However, a reliable methodology allowing assessment of effective connectivity (EC) at the whole-brain level and across high-resolution parcellations has remained elusive. This limitation reflects a trade-off between model detail and scalability. Mechanistic approaches such as Dynamic Causal Modeling (DCM) provide a principled description of directed interactions but become impractical for large parcellations, whereas scalable methods based on linear-system inversion often fail to reliably recover key features of the underlying effective connectivity. Here, using data from the Human Connectome Project S1200 release, we show that the multivariate Ornstein-Uhlenbeck (mOU) approach for EC estimation can be adapted to yield EC estimates that are in broad agreement with those of an advanced DCM, at least when the spatial variability of the hemodynamic response falls within the physiological range characteristic of the healthy adult brain. Furthermore, thanks to its low computational cost, mOU can be easily scaled up to large neuroimaging cohorts and high-resolution parcellations. High-resolution, mOU-based EC outperforms functional connectivity for single-subject connectome fingerprinting. In addition, it enables a high-resolution characterization of the brains hierarchical organization and modular architecture by identifying directed interactions within and between communities and revealing driver and receiver regions. Overall, our methodology provides a reliable and scalable EC estimation pipeline of wide applicability to cognitive and network neuroscience.

neuroscience↗

bioRxiv2024

Transitions from monotonic to tuned responses in recurrent neural network models during timing prediction

The brain exhibits a gradual transition in responses to visual event duration and frequency through the visual processing hierarchy: from monotonically increasing to timing-tuned responses. Over their hierarchies, properties of both response types are progressively transformed. Here, we implement simulations based on artificial neural networks to investigate the requirements of neural systems for the emergence of such responses and their properties transformations. We see that recurrent networks develop monotonic responses whose properties progressions over network layers resemble those over brain areas. Responses to another sensory quantity, Furthermore, recurrent networks can further develop tuned responses, but only with training, a gradual transition between monotonic and tuned responses emerges. Particularly, if this training is done on predictable sequences, the tuned properties progressions resemble those observed in the brain. These results suggest that the emergence of visual timing-tuned responses and the subsequent hierarchical transformations of these responses result from recurrent neural computation and predictive processing of sensory event timing.

neuroscience↗

bioRxiv2023

Susceptibility to multitasking in chronic stroke is associated to damage of the multiple demand system and leads to lateralized visuospatial deficits

Functional impairment after stroke is related to the amount of brain damage but there is no strict correspondence between lesion and magnitude of the deficit or its recovery. Theoretical constructs such as cognitive or brain reserve have been invoked as unspecific protective factors to explain this mismatch. Here, we consider the opposite point of view, that is the instances in which this protection is lost or overturned. Several studies have shown - in domains encompassing sensory, motor, and cognitive deficits - that paradigms in which the inherent processing limits of the brain are stressed (e.g., by introducing multitasking and attentional load), are indeed capable to unveil the presence of deficits that are otherwise missed. We administered a computerized multitasking paradigm to a sample of 46 consecutive patients with first-ever unilateral subacute to chronic stroke and no sign of lateralized spatial-attentional disorders according to established diagnostic tests. We then used cluster analysis to classify patients, in a purely data-driven manner, according to their multivariate pattern of performance across different conditions (e.g., single- vs dual-tasking, ipsi- vs. contra-lesional stimuli). This enabled us to identify, within a group of putatively spared patients, a cluster of individuals presenting with stark contralesional biases of spatial awareness exclusively in conditions of concurrent attentional load, i.e. a phenotype characterised by high susceptibility to multitasking. This construct was also captured by a latent factor obtained from principal component analysis, providing a continuous susceptibility index across the whole sample. In spite of similar lesion volume, patients in the high susceptibility cluster presented worse performance in activities of daily living and neuropsychological tests evaluating domain-general abilities spanning attention, executive functions, and reasoning. Multivariate predictive modeling based on lesions location and structural disconnections revealed distinctive correlates of high sensitivity to multitasking in the Multiple-Demand (MD) System, a network of frontal and fronto-parietal areas subserving domain general processes. Damage in these areas may critically interact with domain-specific ones (e.g., devoted to spatial attention), resulting in subtle, but significant difficulties for patients in everyday life situations. In conclusion, the construct of susceptibility to multitasking has the promising potential to provide us a better understanding of what marks the passage, after brain damage, to clinically visible deficits. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/561866v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@a1bc89org.highwire.dtl.DTLVardef@60cfedorg.highwire.dtl.DTLVardef@1f2ed85org.highwire.dtl.DTLVardef@1c7e5ec_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Pupil size as a robust marker of attentional capture by nicotine-related stimuli in smokers

Spatial attention can be magnetically attracted by behaviorally salient stimuli. This phenomenon occasionally conflicts with behavioral goals, leading to maladaptive consequences, as in the case of addiction, in which attentional biases have been described and linked with clinically meaningful variables, such as craving level or dependence intensity. Here, we sought to probe the markers of attentional priority in smokers through eye-tracking measures, by leveraging the established link between eye movements and spatial attention. We were particularly interested in potential markers related to pupil size, because pupil diameter reflects a range of autonomic, affective, and cognitive/attentional reactions to behaviorally significant stimuli and is a robust marker of appetitive and aversive learning. We found that changes in pupil size to nicotine-related visual stimuli could reliably predict, in crossvalidated logistic regression, the smoking status of young smokers (showing pupil constriction) better than more traditional proxy measures. The possibility that pupil constriction may reflect a bias toward central vision, e.g. attentional capture, is discussed in terms of sensory tuning with respect to nicotine-related stimuli. Pupil size was more sensitive at lower nicotine dependence levels, and at increased abstinence time (though these two variables were collinear). We conclude that pupillometry can provide a robust marker for attentional priority computation and useful indications regarding motivational states and individual attitudes toward conditioned stimuli.

neuroscience↗

bioRxiv2020

Recovery of neural dynamics criticality in personalized whole brain models of stroke

The critical brain hypothesis states that biological neuronal networks, because of their structural and functional architecture, work near phase transitions for optimal response to internal and external inputs. Criticality thus provides optimal function and behavioral capabilities. We test this hypothesis by examining the influence of brain injury (strokes) on the criticality of neural dynamics estimated at the level of single participants using directly measured individual structural connectomes and whole-brain models. Lesions engender a sub-critical state that recovers over time in parallel with behavior. The improvement of criticality is associated with the re-modeling of specific white matter connections. We show that personalized whole-brain dynamical models poised at criticality track neural dynamics, alteration post-stroke, and behavior at the level of single participants.

neuroscience↗