Biology subjects

Saviola, F.

Publications and source records attributed to Saviola, F..

6 recordsLinked to original sources

bioRxiv2026

Cortical long-range dynamics track excitation-inhibition balance across cognitive states and organizational principles

A fundamental question in systems neuroscience is how the brain's excitation-inhibition (E/I) balance shapes the temporal structure of neural activity and whether we can non-invasively recover this relationship from neural scaling properties. We show that MRS-derived E/I balance is expressed in long-range temporal dynamics and constrained by cytoarchitecture. The laminar organisation of inhibitory circuits, spanning agranular to granular cortex, determines how faithfully regional dynamics reflect E/I balance. This neurochemical signature is state-dependent: its relationship with cortical dynamics reverses during task engagement, consistent with traversal of dynamical regimes around a critical point. It extends beyond fMRI to arousal-linked dynamics, indexed by pupil diameter and microsaccades, indicating a global mode of cortical state rather than a regionally isolated property. The imprint of E/I balance on long-range dynamics follows the sensory-association axis at rest, and is reconfigured during task engagement. These findings establish E/I balance as a neurochemical quantity with structured, scalable signatures across the temporal and spatial organization of the human cortex.

neuroscience↗

bioRxiv2026

Directed Brain Connectomics Revealed by Bicommunity Structure

The brain is a complex, interconnected system in which structural wiring underpins the flow of information between functional units. After two decades of advances in connectomics, the asymmetry of edge structures in large-scale brain networks remains largely unexplored. Here, we build a directed connectome combining structure and function, and dissect its organization into bicommunities where sending and associated receiving sets of nodes, through clustering of directed-edge information, are fully acknowledged. Our findings reveal a primary directional axis from sensory to association cortices, indicative of bottom-up information flow. Validation with invasive electrophysiological measures demonstrates that these bicommunities capture an intermediate organizational scale at which connection asymmetry aligns across modalities. Finally, sets of distinct known anatomical fiber bundles and their functional mapping are recovered as specific bicommunities. This work identifies asymmetric pathways onto which neural computation can be expressed, and establishes directed connectomics as a novel framework for understanding brain organization.

neuroscience↗

bioRxiv2024

Multiscale excitation-inhibition balance dynamics: integrating metabolite kinetics with time-varying executive networks

The balance between neural excitation and inhibition (EIB) is an essential mechanism supporting cognitive processes. Yet, little is understood about how EIB shifts with cognitive load and its impact on functional connectivity dynamics. In this study, we investigate temporal profiles of the reciprocal modulation between EIB and functional network dynamics during working memory tasks, revealing that EIB prefrontal kinetics scale with increasing cognitive load. Notably, prefrontal EIB kinetics correlated with cognitive load, impacting stability of networks crucial for cognitive function. On one hand, brain dynamics adapt to meet increasing cognitive challenges with a shift towards more focused and sustained neural activity patterns in terms of connectivity. On the other, imbalances favouring excitation may hinder cognitive adaptability. Importantly, this experimental approach demonstrates a link between EIB kinetics, brain network dynamics and cognitive performance, defining the groundwork for exploring healthy and aberrant cognitive states. TeaserHighly focused or less responsive? Chemical signalling and network dynamics are coupled to produce persistent cognitive states.

neuroscience↗

bioRxiv2023

TR(acking) individuals down: exploring the effect of temporal resolution in resting-state functional MRI fingerprinting

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisitions temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s. We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make substantial contributions to subject identifiability, whereas sensory-motor regions become influential only when integrating data from different TRs. We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signals sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

neuroscience↗

bioRxiv2022

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson's disease cohorts

An open discussion in studies of intrinsic brain functional connectivity is the mitigation of head motion-related artifacts, particularly in the presence of peculiar symptomatology such as in Parkinsons disease (PD). Previous studies show that Independent Component Analysis (ICA) denoising improves the reproducibility of functional connectivity findings by detecting sources of non-neural signals. However, there is still no consensus about which pre-processing pipeline should be applied in natural high motion populations such as PD, particularly in relation to novel functional network descriptions derived from dynamic connectivity analyses. In this study, we investigated how different pre-processing pipelines affect intrinsic brain connectivity metrics, both static and dynamic, derived from a group of young healthy controls (HC) and a group of PD participants. A total of 20 HC and 20 PD subjects participated in this 3 T MRI study. Resting-state functional MRI images were used to test the effects of the pre-processing pipeline of static (sFC) and temporal-varying functional connectivity (dFC) estimations. Both MRI datasets were pre-processed using three different workflows differing in the motion correction approach: (i) standard motion realignment (mc); (ii) motion outlier detection and deweighting based on image intensity change estimations (DVARS) and (iii) ICA-based noise removal using reference noise features (AROMA). Furthermore, the PD dataset was also processed with a fourth method by applying an ICA-based denoising (FIX), previously trained on the HC group. sFC analysis was performed using Group ICA, by temporally concatenating different pre-processing types in pairs of different runs. Two types of dFC analyses were considered: innovation-driven co-activation patterns (iCAPs) and co-activation patterns (CAPs). CAPs allow dFC estimations that do not require the deconvolution of the hemodynamic response function and its derivative, thus potentially being less sensitive to head-motion related noise. We found that regardless of substantial head motion differences in the two groups, sFC results were consistent across denoising strategies. Conversely, dFC was extremely sensitive to denoising strategies, particularly for the PD group with the transient-based dFC analyses. Indeed, the use of the peak-based dFC framework enables the detection of time-varying networks but in a way that is highly dependent on the motion correction pipeline. In conclusion, we show that dynamic functional network representations are highly sensitive to both head motion and to fMRI denoising methods. These findings stress the importance of considering and reporting these experimental aspects to help with the reproducibility and interpretation of different studies. Future work is needed to further investigate transient-based dFC strategies that are more robust to head motion.

neuroscience↗

bioRxiv2020

Did I imagine that? The functional role of paracingulate cortex in reality monitoring

Reality monitoring describes our ability to distinguish between internally and externally generated experiences. Individuals show significant variation in this ability and impaired reality monitoring has been linked to the experience of hallucinations. We undertook two studies to investigate the association between reality monitoring and morphology of the paracingulate region of medial prefrontal cortex. In Study 1 we compared reality monitoring accuracy and functional connectivity within paracingulate cortex in groups of healthy controls (N=20) and patients with schizophrenia and hallucinations (N=19). Controls showed greater reality monitoring accuracy that was associated with resting-state functional connectivity between paracingulate, precuneus and occipital cortices, while reality monitoring in patients was associated with more lateral functional connectivity. In Study 2 we used real-time fMRI neurofeedback to obtain causal evidence for the role of the paracingulate cortex in reality monitoring. Healthy individuals received Active feedback from paracingulate cortex (N=21) or Sham feedback based on randomised signal (N=18). Active-group participants showed a specific behavioural effect of improved reality monitoring for Imagined items, as well as increases in both activity within the paracingulate region, and its posterior functional connectivity with precuneus and lateral parietal cortices, and occipital cortex. Our findings suggest reality monitoring in healthy individuals is causally supported by a paracingulate mediated flexible network including the precuneus. Network connectivity can be enhanced using neurofeedback and tracks with improved reality monitoring ability. In contrast, patients with schizophrenia may utilise a distinct and more lateral network which may explain observed sub-optimal reality monitoring accuracy, contributing to the experience of hallucinations. Significance StatementReality monitoring refers to our ability to distinguish imagination from our experiences in the outside world, and is linked both to hallucinations in schizophrenia as well as to the morphology of paracingulate cortex area of the brain. Here, we revealed less paracingulate involvement in the functional reality monitoring networks in patients with schizophrenia compared to healthy individuals. Thereafter, we used real-time fMRI neurofeedback to show that healthy individuals can learn to upregulate brain activity within the paracingulate cortex, with this resulting in both improved reality monitoring ability and changes in paracingulate functional connectivity. This suggests that paracingulate cortex activity and connectivity play a causal role in reality monitoring, with implications for both the understanding and treatment of hallucinations.

neuroscience↗